Energizing Change: Electric Vehicle Rise in Switzerland

Author

Pierre Vernet, Alexis Jeanneret, Hugo Troendle, Urs Hurni

Published

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1 Introduction

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1.1 Background and Motivation

In an era marked by unprecedented environmental challenges, our world is at a critical juncture where sustainable practices are no longer an option but a necessity. Climate change, driven by anthropogenic activities, poses an imminent threat to the delicate ecological balance. Recognizing the urgency of this global issue, we are compelled to investigate the specific context of Switzerland, the country we live in, and which has a lot of financial capacities to change rapidly.

The automotive sector plays a pivotal role in shaping the a sustainable world, and our focus on electric vehicle adoption in Switzerland reflects a strategic choice to comprehend the intricate interplay of factors driving this transition.

The motivations underlying our research stem from a multifaceted perspective. Firstly, the automotive industry is undergoing a transformative shift globally, with electric vehicles emerging as a key solution to reduce carbon emissions. By narrowing our focus to Switzerland, we aim to provide nuanced insights into the factors influencing EV adoption, offering a unique perspective on the socio-economic, environmentally stable and financially comfortable Swiss context. The adoption of electric vehicles provides an insight into people’s inclination to make efforts towards sustainability. This emphasizes the crucial role that electric vehicle adoption plays in evaluating society’s commitment to sustainable practices.

Secondly, our commitment to environmental sustainability extends beyond a generic interest. The urgency of addressing climate change has never been more palpable, and our research seeks to contribute to the discussion on sustainable mobility.

Moreover, we believe the future is encapsulated in our dedication to understanding the dynamics of environmentally conscious consumer behavior. By unraveling the complexities of EV adoption in Switzerland over the years (2005-2022), we aim to offer valuable insights that could guide policymakers, businesses, and consumers towards more informed and sustainable choices.

We believe that by comprehensively analyzing the impact of external factors on the adoption of electric vehicles in Switzerland, we can contribute meaningfully to the ongoing global efforts towards a more sustainable and resilient future. Through this project, we aspire to inspire positive change, foster innovation, and advocate for a paradigm shift towards eco-friendly practices in the automotive sector and beyond.

1.3 Reseach Questions

1. Given fluctuations in oil prices, demographic shifts, and major policy, which of these factors have a direct causal impact on the shifts in the adoption of electric vehicles in Switzerland?
1. Based on past electric vehicle adoption trends in Switzerland, can we forecast future adoption rates and pinpoint times of significant increases or decreases correlated with major events or policy changes?
1. In comparing regions in Switzerland, which areas show higher or lower adoption of electric vehicles, and how does this regional adoption align or vary with external factors like oil price changes, political opinions, and demographic shifts?
1. How has the growth of electric vehicles evolved in comparison to other countries such as France, and what factors might account for the differences in their evolution ?
1. To what extent does the evolution in the availability of charging stations exert an influence on the adoption of electric vehicles in Switzerland?

2 Data

2.1 Raw Datasets

Use a Loop for Similar Files.

Check for Large Data: If any of our datasets are particularly large (like the swiss_vehicle), we used a more efficient data reading librariy data.table (with fread) to speed up the data loading process.

We also added a common file path prefix (“../data/”) to make the code cleaner and easier to change for any usage in the future.

Code

library(data.table)
file_path <- "../data/"

google_trends_files <- paste0(file_path, "googletrends_", c("auto-elettrica", "elektro-auto", "elektrofahrzeug", "elektromobil", "eletric-car", "EV", "vehicule-electrique", "voiture-electrique"), "_2005-2022.csv")
google_trends_data <- lapply(google_trends_files, fread)
vehicle_data_2005_2008 <- fread(paste0(file_path, "road_vehicle_CH_2005-2008.csv"))
vehicle_data_2009_2022 <- fread(paste0(file_path, "road_vehicle_CH_2009-2022.csv"))
oil_prices_data <- fread(paste0(file_path, "BrentOilPrices.csv"))
demographic_data <- fread(paste0(file_path, "demographic.csv"))
charging_station <- fread(paste0(file_path, "charging_station.csv"))
france_v <- read_excel(paste0(file_path, "parc_vp_france_2022.xlsx"), sheet = 2)
political_data_sheets_prep <- excel_sheets(paste0(file_path, "political_data.xlsx"))
df_swisspop_2022 <- read_excel(paste0(file_path, "swiss_pop.xlsx"), sheet = 1)
df_swisspop_2021 <- read_excel(paste0(file_path, "swiss_pop.xlsx"), sheet = 2)
df_swisspop_2020 <- read_excel(paste0(file_path, "swiss_pop.xlsx"), sheet = 3)
df_swisspop_2019 <- read_excel(paste0(file_path, "swiss_pop.xlsx"), sheet = 4)
df_swisspop_2018 <- read_excel(paste0(file_path, "swiss_pop.xlsx"), sheet = 5)
charge_ch_fr <- fread(paste0(file_path,"df_charging_points_CH_FR.csv"))

For this phase of the project, our approach involves presenting diverse datasets in a table form. To achieve this, we used the kable function from the kableExtra package, resulting in a table that displays each variable along with its corresponding definition and also added some bootstrap_otions for different stlyles.

2.1.1 Swiss Vehicle Registration Dataset

This data set explains the new registrations of road vehicles by vehicle group, canton, vehicle type, fuel, month and year in Switzerland. This data set is essential to evaluate the EVs’ evolution within our chosen location.

Code

# Create a tibble with variable descriptions
variable_table_vehicle <- tibble(
  Variable = c("Canton", "Vehicle Group / Type", "Fuel", "Month", "2009-2022"),
  Description = c(
    "The region in Switzerland.",
    "Type or group of the vehicle.",
    "Type of fuel used by the vehicle.",
    "The month of the data.",
    "Number of vehicles for each respective year. Each with its own column"
  )
)

# Display the table using kableExtra
variable_table_vehicle %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover"))

Variable	Description
Canton	The region in Switzerland.
Vehicle Group / Type	Type or group of the vehicle.
Fuel	Type of fuel used by the vehicle.
Month	The month of the data.
2009-2022	Number of vehicles for each respective year. Each with its own column

Source - bfs.admin.ch

2.1.2 Oil price Dataset

This data set explains the evolution of the price of brent oil over time. From this data set, we will be able to obtain precious information to compute our linear regressions concerning oil price and EVs adoptions.

Code

# Create a tibble with variable descriptions
variable_table <- tibble(
  Variable = c("Date", "Price"),
  Description = c(
    "The date when the price was recorded. In a day-month-year format.",
    "The price of Brent Oil on the given date."
  )
)

# Display the table using kableExtra
variable_table %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover", "condensed"))

Variable	Description
Date	The date when the price was recorded. In a day-month-year format.
Price	The price of Brent Oil on the given date.

Source - Kaggle.com

2.1.3 Swiss Demographics Dataset

While this dataset contains a substantial amount of information, only a fraction of it will be pertinent to our research. It explains the evolution of the Swiss population over time through variables given below.

Code

# Create a tibble with variable descriptions
variable_table <- tibble(
  Variable = c(
    "Year", "Citizenship (category)", "Sex", "Age", "Population on 1 January", 
    "Live birth", "Death", "Natural change", 
    "Immigration incl. change of population type", "Emigration", 
    "Net migration incl. change of population type", 
    "Change of population type", "Acquisition of Swiss citizenship", 
    "Gender change in the civil register (entry)", 
    "Gender change in the civil register (exit)", "Statistical adjustment", 
    "Population on 31 December", "Population change"
  ),
  Description = c(
    "The year of the demographic data.",
    "The categorization of the citizenship status.",
    "Gender category.",
    "Age category.",
    "Population count at the beginning of the year.",
    "Number of births in the year.",
    "Number of deaths in the year.",
    "Change in the population due to births and deaths.",
    "Number of immigrants, including change of population type.",
    "Number of emigrations.",
    "Net migration count, including change of population type.",
    "Change in the categorization of the population.",
    "Number of individuals who acquired Swiss citizenship.",
    "Number of gender changes registered (entry).",
    "Number of gender changes registered (exit).",
    "Adjustments made to the data for accuracy.",
    "Population count at the end of the year.",
    "Change in population over the year."
  )
)

# Display the table using kableExtra
variable_table %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover", "condensed"))

Variable	Description
Year	The year of the demographic data.
Citizenship (category)	The categorization of the citizenship status.
Sex	Gender category.
Age	Age category.
Population on 1 January	Population count at the beginning of the year.
Live birth	Number of births in the year.
Death	Number of deaths in the year.
Natural change	Change in the population due to births and deaths.
Immigration incl. change of population type	Number of immigrants, including change of population type.
Emigration	Number of emigrations.
Net migration incl. change of population type	Net migration count, including change of population type.
Change of population type	Change in the categorization of the population.
Acquisition of Swiss citizenship	Number of individuals who acquired Swiss citizenship.
Gender change in the civil register (entry)	Number of gender changes registered (entry).
Gender change in the civil register (exit)	Number of gender changes registered (exit).
Statistical adjustment	Adjustments made to the data for accuracy.
Population on 31 December	Population count at the end of the year.
Population change	Change in population over the year.

Source - bfs.admin.ch

2.1.4 Google trend data

This Data set shows the evolution of Google trends on 8 different terms. We chose these terms in all 3 different main Swiss national languages (French, Italian and German) to make sure we get the most accurate data possible. Those variables will help us highlight potential tendencies/trends.

Code

# Create a tibble with variable descriptions for Google Trends data
variable_table_google_trend <- tibble(
  Variable = c("Month", 
                "Search Term: Voiture électrique", 
                "Search Term: Véhicule électrique", 
                "Search Term: EV", 
                "Search Term: Electric Car", 
                "Search Term: Elektromobil", 
                "Search Term: Elektrofahrzeug", 
                "Search Term: Elektro Auto", 
                "Search Term: Auto Elettrica"),
  Description = c(
    "The month of the data",
    "Google Trends data for 'Voiture électrique: (Suisse)'",
    "Google Trends data for 'Véhicule électrique: (Suisse)'",
    "Google Trends data for 'EV: (Suisse)'",
    "Google Trends data for 'Electric car: (Suisse)'",
    "Google Trends data for 'Elektromobil: (Suisse)'",
    "Google Trends data for 'Elektrofahrzeug: (Suisse)'",
    "Google Trends data for 'Elektro Auto: (Suisse)'",
    "Google Trends data for 'Auto Elettrica: (Suisse)'"
  )
)

# Display the table using kableExtra
variable_table_google_trend %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover", "condensed"))

Variable	Description
Month	The month of the data
Search Term: Voiture électrique	Google Trends data for 'Voiture électrique: (Suisse)'
Search Term: Véhicule électrique	Google Trends data for 'Véhicule électrique: (Suisse)'
Search Term: EV	Google Trends data for 'EV: (Suisse)'
Search Term: Electric Car	Google Trends data for 'Electric car: (Suisse)'
Search Term: Elektromobil	Google Trends data for 'Elektromobil: (Suisse)'
Search Term: Elektrofahrzeug	Google Trends data for 'Elektrofahrzeug: (Suisse)'
Search Term: Elektro Auto	Google Trends data for 'Elektro Auto: (Suisse)'
Search Term: Auto Elettrica	Google Trends data for 'Auto Elettrica: (Suisse)'

Sources for Google Trends Data: - Voiture électrique Trends - Véhicule électrique Trends - EV Trends - Electric Car Trends - Elektromobil Trends - Elektrofahrzeug Trends - Elektro Auto Trends - Auto Elettrica Trends

2.1.5 French vehicle registration data

We decided to add this data set to help us compare with another country. Indeed, France is an adjacent country which might imply potential similarities in the outcome. This data set is made of variables such as the Date of the French vehicle registration or whether it is from a professional user or not. The information we are interested in concern the number of registered vehicles in the country by propulsion method over time.

Code

# Create a tibble with variable descriptions for vehicle categorization data
variable_table <- tibble(
  Variable = c("Date", "Ensemble des voitures particulieres", "Professionnel", "Particulier", "Crit'Air"),
  Description = c(
    "The date, in year format, in which the observations are registered",
    "The sum of vehicle in every groups",
    "Category 'Professional' of vehicle",
    "Category 'Passenger Car' of vehicle",
    "Categorizes vehicles by emissions with from best to worst classes"
  )
)

# Display the table using kableExtra
variable_table %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover", "condensed"))

Variable	Description
Date	The date, in year format, in which the observations are registered
Ensemble des voitures particulieres	The sum of vehicle in every groups
Professionnel	Category 'Professional' of vehicle
Particulier	Category 'Passenger Car' of vehicle
Crit'Air	Categorizes vehicles by emissions with from best to worst classes

Source - Auto Elettrica Trends

2.1.6 Charging Stations

This data set is meant to determine the number of charging stations per canton, per charging power, per month, per plug type in Switzerland since November 2020. It is important to mention that a station can have multiple charging points. These values will help us to obtain more information about the Swiss adaption to EVs and their potential effects on EVs adoption by the Swiss population

Code

# Create a tibble with variable descriptions in English for charging station data
variable_table_charging <- tibble(
  Variable = c("year",
                "month",
                "stations_CH_count",
                "stations_XY_count",
                "locations_CH_count",
                "locations_XY_count",
                "plugs_CH_count",
                "plugs_XY_count",
                "chargingPower_CH_sum",
                "chargingPower_XY_sum",
                "chargingPower_CH_count",
                "chargingPower_XY_count",
                "chargingPower_10kW_count",
                "chargingPower_21kW_count",
                "chargingPower_42kW_count"),
  Description = c(
    "Year of the key figures collection.",
    "Month of the key figures collection. These are collected daily and published monthly.",
    "Number of available charging stations in Switzerland. A station can have multiple charging points.",
    "Number of charging stations per canton. A station can have multiple charging points. This attribute is available for all cantons, XY representing the official canton abbreviation.",
    "Number of stations in Switzerland. A station can have multiple charging points.",
    "Number of stations per canton. A station can have multiple charging points. This attribute is available for all cantons, XY representing the official canton abbreviation.",
    "Number of charging plugs in Switzerland.",
    "Number of charging plugs per plug type in Switzerland. This attribute is available for all plug types, XY corresponding to the plug type according to the Open Intercharge Protocol (OICP) version 2.2 (see below).",
    "Total maximum charging power in kilowatts of recharge stations in Switzerland. Only charging points with known power are considered (see chargingPower_CH_count).",
    "Total maximum charging power in kilowatts of recharge stations in a canton. Only charging points with known power are considered (see chargingPower_XY_count).",
    "Number of recharge stations in Switzerland with known maximum charging power and for which the total maximum charging power has been taken into account (chargingPower_CH_sum).",
    "Number of recharge stations per canton with known maximum charging power and for which the total maximum charging power has been taken into account (chargingPower_XY_sum).",
    "Number of recharge stations in Switzerland with known maximum charging power delivering a maximum charging power of 10 kW.",
    "Number of recharge stations in Switzerland with known maximum charging power ranging from over 10 kW to 21 kW.",
    "Number of recharge stations in Switzerland with known maximum charging power ranging from over 21 kW to 42 kW."
  )
)

# Display the table using kableExtra
variable_table_charging %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover", "condensed"))

Variable	Description
year	Year of the key figures collection.
month	Month of the key figures collection. These are collected daily and published monthly.
stations_CH_count	Number of available charging stations in Switzerland. A station can have multiple charging points.
stations_XY_count	Number of charging stations per canton. A station can have multiple charging points. This attribute is available for all cantons, XY representing the official canton abbreviation.
locations_CH_count	Number of stations in Switzerland. A station can have multiple charging points.
locations_XY_count	Number of stations per canton. A station can have multiple charging points. This attribute is available for all cantons, XY representing the official canton abbreviation.
plugs_CH_count	Number of charging plugs in Switzerland.
plugs_XY_count	Number of charging plugs per plug type in Switzerland. This attribute is available for all plug types, XY corresponding to the plug type according to the Open Intercharge Protocol (OICP) version 2.2 (see below).
chargingPower_CH_sum	Total maximum charging power in kilowatts of recharge stations in Switzerland. Only charging points with known power are considered (see chargingPower_CH_count).
chargingPower_XY_sum	Total maximum charging power in kilowatts of recharge stations in a canton. Only charging points with known power are considered (see chargingPower_XY_count).
chargingPower_CH_count	Number of recharge stations in Switzerland with known maximum charging power and for which the total maximum charging power has been taken into account (chargingPower_CH_sum).
chargingPower_XY_count	Number of recharge stations per canton with known maximum charging power and for which the total maximum charging power has been taken into account (chargingPower_XY_sum).
chargingPower_10kW_count	Number of recharge stations in Switzerland with known maximum charging power delivering a maximum charging power of 10 kW.
chargingPower_21kW_count	Number of recharge stations in Switzerland with known maximum charging power ranging from over 10 kW to 21 kW.
chargingPower_42kW_count	Number of recharge stations in Switzerland with known maximum charging power ranging from over 21 kW to 42 kW.

Source - [IEA.org] (https://www.iea.org/data-and-statistics/data-tools/global-ev-data-explorer) ### 2.2.7 Complementary charging station

Code

# Create a tibble with variable descriptions for the Swiss population data set
variable_charging_station_v2 <- tibble(
  Variable = c("Region", "Category", "Parameter", "Mode","Powertrain", "Unit","Value"),
  Description = c(
    "Either Switzerland or France",
    "One category: Historical",
    "One parameter: EV charging point",
    "One mode: EV",
    "Either Publicly available fast, or Publicly availabe slow",
    "One unit: charging point",
    "The total number of available charging point for a given region, year and powertrain"
  )
)

# Display the table using kableExtra
variable_charging_station_v2 %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover"))

Variable	Description
Region	Either Switzerland or France
Category	One category: Historical
Parameter	One parameter: EV charging point
Mode	One mode: EV
Powertrain	Either Publicly available fast, or Publicly availabe slow
Unit	One unit: charging point
Value	The total number of available charging point for a given region, year and powertrain

Source - bfe.admin.ch

2.2.8 Political strength per canton

Code

# Create a tibble with variable descriptions for the Swiss population data set
variable_table_politic <- tibble(
  Variable = c("Years 1971-2023", "Canton", "Political parties", "Taux de participation"),
  Description = c(
    "The years were elections to the Conseil National took place from 1971 to 2023",
    "The name of the canton in Switzerland",
    "The name of all political parties in Switzerland and their strength in a given Canton, (Canton's total = 100%), in a given   year",
    "The participation rate to the elections of the Conseil National in a given year"
  )
)

# Display the table using kableExtra
variable_table_politic %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover"))

Variable	Description
Years 1971-2023	The years were elections to the Conseil National took place from 1971 to 2023
Canton	The name of the canton in Switzerland
Political parties	The name of all political parties in Switzerland and their strength in a given Canton, (Canton's total = 100%), in a given year
Taux de participation	The participation rate to the elections of the Conseil National in a given year

Source - nom de la source

2.2.7 Swiss Population

Code

# Create a tibble with variable descriptions for the Swiss population dataset
variable_table_swiss_pop <- tibble(
  Variable = c("Canton", "Total Population", "Age Groups", "Sex", "Citizenship", "Marital Status", "Typology: Area with urban character"),
  Description = c(
    "The name of the canton in Switzerland.",
    "Total number of permanent residents in the canton.",
    "Population broken down into age groups: 0–19, 20–64, 65 and over.",
    "Population broken down by sex: Male and Female.",
    "Population broken down by citizenship: Swiss and Foreigner.",
    "Population broken down by marital status: Single, Married, Widowed, Divorced, Unmarried, In a registered partnership, Partnership dissolved.",
    "Typology of the area based on urban character: Urban core, Area influenced by urban cores, Area beyond urban influence."
  )
)

# Display the table using kableExtra
variable_table_swiss_pop %>%
  kbl() %>%
  kable_styling(position = "center", bootstrap_options = c("striped", "bordered", "hover"))

Variable	Description
Canton	The name of the canton in Switzerland.
Total Population	Total number of permanent residents in the canton.
Age Groups	Population broken down into age groups: 0–19, 20–64, 65 and over.
Sex	Population broken down by sex: Male and Female.
Citizenship	Population broken down by citizenship: Swiss and Foreigner.
Marital Status	Population broken down by marital status: Single, Married, Widowed, Divorced, Unmarried, In a registered partnership, Partnership dissolved.
Typology: Area with urban character	Typology of the area based on urban character: Urban core, Area influenced by urban cores, Area beyond urban influence.

Source - bfe.admin.ch

2.2 Data Wrangling

2.2.1 Data Wrangling : Oil clean

Initially, while attempting to format the dataset, we discovered the generation of numerous NAs. Upon delving deeper into the data, we identified two distinct date formats: the first being [DD-MMM-YYYY] and the second [MMM DD, YYYY]. Consequently, the initial date formatting was ineffective. Therefore, we undertook the task of matching the two tables with the same date format.

Another issue we encountered was to present the date in a standardized form across all our dataset to perform time series analyses. For that, we had to change the abbreviated months names (i.e. Jan, Feb, etc.) into numbers.

Code

# Format 1: DD-MMM-YYYY (e.g., 15-Apr-2020)
oil_df_1 <- oil_prices_data[1:8360,] %>%
  separate(Date, into = c("Day", "Month", "Year"), sep = "-") %>%
  mutate(Date = dmy(paste(Day, Month, Year)))

# Format 2: MMM DD, YYYY (e.g., Apr 22, 2020)
# Handling the separator with space and comma
oil_df_2 <- oil_prices_data[8361:nrow(oil_prices_data),] %>%
  separate(Date, into = c("Month", "Day", "Year"), sep = " ", extra = "merge") %>%
  mutate(Day = word(Day, 1),  # Extracts just the day part
         Date = mdy(paste(Month, Day, Year)))

# Merge and filter for dates after 2005
df_oil <- rbind(oil_df_1, oil_df_2) %>%
  filter(year(Date) >= 2005) %>%
  select(Date, Price)

# Display cleaned data
reactable(
  df_oil,  # Use your oil prices dataframe
  columns = list(
    Date = colDef(
      align = "center",  # Center align the date column
      minWidth = 150
    ),
    Price = colDef(
      align = "right",  # Right align the price column
      style = function(value) {
        # Apply conditional formatting based on the price value
        if (value > 120) {
          color <- "green"  # Green for high prices
        } else if (value < 30) {
          color <- "red"    # Red for low prices
        } else {
          color <- "black"  # Default color for other values
        }
        list(color = color)
      }
    )
  ),
  highlight = TRUE,  # Highlight rows on hover
  defaultPageSize = 10,  # Display 10 rows per page
  paginationType = "numbers",  # Use numbers for page navigation
  searchable = TRUE,  # Make the table searchable
  sortable = TRUE,  # Allow sorting
  resizable = TRUE  # Allow column resizing
)

2.2.2 Data Wrangling : Google trend clean

Comprehending Google Trends information involves understanding the contextualization of the provided figures. The given data is standardized which limits the range of information we can obtain from it.

The crucial element for effective narration lies in utilizing the normalized Trends data. This normalization indicates that when observing search interest trends over time, it is interpreted as a ratio relative to all searches conducted on all topics on Google during that specific period and location. Likewise, when assessing regional search interest for a specific topic, it is construed as the search interest for that particular topic in a given region relative to all searches on all topics on Google in that same place and time.

Source

To clean and process eight Google Trends datasets related to different search terms, we created a function process_dataset() that reads each CSV file, removes date indices, excludes the first row, checks for missing values, renames columns, converts types, and standardizes the date format. Then, we iterated through each dataset, merged them based on the “Date” column, summed values for each search term per date, and normalized these values between 1 and 100.

Code

dataset_names <- c(
  "googletrends_auto-elettrica_2005-2022.csv",
  "googletrends_elektro-auto_2005-2022.csv",
  "googletrends_elektrofahrzeug_2005-2022.csv",
  "googletrends_elektromobil_2005-2022.csv",
  "googletrends_eletric-car_2005-2022.csv",
  "googletrends_EV_2005-2022.csv",
  "googletrends_vehicule-electrique_2005-2022.csv",
  "googletrends_voiture-electrique_2005-2022.csv"
)

# Function to process each dataset
process_dataset <- function(file_path) {
  # Read the dataset
  google_trends_data <- read.csv(file_path)

  # Remove the dates from the index
  google_trends_data <- google_trends_data |> rownames_to_column(var = "Date")

  # Remove the first row using slice
  google_trends_data <- slice(google_trends_data, -1)

  # Check for NA
  cat("NA count for", file_path, ": ", sum(is.na(google_trends_data$Date)), "\n")
  cat("NA count for SearchCount in", file_path, ": ", sum(is.na(google_trends_data$Catégorie...Toutes.catégories)), "\n")

  # Rename col
  colnames(google_trends_data)[2] <- "SearchCount"

  # Convert to numeric
  google_trends_data$SearchCount <- as.numeric(google_trends_data$SearchCount)

  # Convert the column to date with the desired format
  google_trends_data$Date <- as.Date(paste(google_trends_data$Date, "01", sep = "-"))

  return(google_trends_data)
}

# Process each dataset and store in a list
processed_datasets <- list()

for (dataset_name in dataset_names) {
  file_path_gt <- file.path("../data", dataset_name)
  processed_dataset <- process_dataset(file_path_gt)
  processed_datasets[[dataset_name]] <- processed_dataset
}
#> NA count for ../data/googletrends_auto-elettrica_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_auto-elettrica_2005-2022.csv :  0 
#> NA count for ../data/googletrends_elektro-auto_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_elektro-auto_2005-2022.csv :  0 
#> NA count for ../data/googletrends_elektrofahrzeug_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_elektrofahrzeug_2005-2022.csv :  0 
#> NA count for ../data/googletrends_elektromobil_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_elektromobil_2005-2022.csv :  0 
#> NA count for ../data/googletrends_eletric-car_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_eletric-car_2005-2022.csv :  0 
#> NA count for ../data/googletrends_EV_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_EV_2005-2022.csv :  0 
#> NA count for ../data/googletrends_vehicule-electrique_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_vehicule-electrique_2005-2022.csv :  0 
#> NA count for ../data/googletrends_voiture-electrique_2005-2022.csv :  0 
#> NA count for SearchCount in ../data/googletrends_voiture-electrique_2005-2022.csv :  0

# Merge datasets based on the "Date" column
merged_data <- reduce(processed_datasets, left_join, by = "Date")

# Sum the values for each search term for a given date
df_gtrends <- merged_data %>%
  rowwise() %>%
  mutate(Sum_SearchCount = sum(c_across(starts_with("SearchCount"))))

# Normalize the Sum_SearchCount values between 1 and 100
df_gtrends$SearchRatio <- df_gtrends$Sum_SearchCount / 8

# Print the result
df_gtrends <- df_gtrends[,-(2:10)]

#display cleaned data
reactable(
  df_gtrends,
  columns = list(
    Date = colDef(
      align = "center",  # Center align the date column
      minWidth = 150
    ),
    SearchRatio = colDef(
      align = "right",  # Right align the SearchRatio column
      style = function(value) {
        if (value > 45) {
          color <- "green"  # Green for values greater than 50
        } else if (value < 5) {
          color <- "red"    # Red for values less than 5
        } else {
          color <- "black"  # Default color for other values
        }
        list(color = color)
      }
    )
  ),
  highlight = TRUE,  # Highlight rows on hover
  defaultPageSize = 10,  # Display 10 rows per page
  paginationType = "numbers",  # Use numbers for page navigation
  searchable = TRUE,  # Make the table searchable
  sortable = TRUE,  # Allow sorting
  resizable = TRUE  # Allow column resizing
)

2.2.3 Cleaning of demographic_data

The main issue with this data set was to transform it in a way that makes it useful for our analysis. We first had to remove all rows containing category summaries in the middle of the data.

We then formulated to following hypothesizes:

Male and Female present the same attitude towards electric vehicles
Swiss and Foreign citizen living in Switzerland present the same attitude towards electric vehicles
People who are not allowed to drive do not have any impact on electric vehicles use

We then decided to do the following cleaning:

Only kept the “Total” for the Male-Female category
Only kept the “Total” for the Swiss-Foreign category
Only kept people aged between 18 and 99. (People aged over 99 account for 0.02% of the population)

Then, instead of keeping a raw data set with all ages between 18 and 99, we decided to group them in widely accepted age segments, namely Generation Z, Millennials, Generation X and Baby boomers. This categorization will help us to analyse whether different generations have a different approach to their mobility or not.

Code

# Clean and process the demographic data
df_demographic <- demographic_data %>%
  filter(Year >= 2005, Year <= 2022) %>%
  filter(str_detect(Sex, "total"), str_detect(`Citizenship (category)`, "total")) %>%
  mutate(Age_clean = gsub(" years", "", Age)) %>%
  filter(!str_detect(Age_clean, "total")) %>%
  mutate(Age_num = as.numeric(Age_clean)) %>%
  filter(!is.na(Age_num), Age_num >= 18, Age_num <= 98) %>%
  select(Year, Age = Age_num, `Population on 1 January`) %>%
  mutate(
    Generation = cut(Age, breaks = c(17, 26, 42, 58, Inf), labels = c("Generation Z", "Millennials", "Generation X", "Baby Boomers"), include.lowest = TRUE),
    Year = ymd(paste(Year, "01", "01", sep = "-")) # Converting Year to Date format
  ) %>%
  group_by(Year, Generation) %>%
  summarise(Population = sum(`Population on 1 January`, na.rm = TRUE)) %>%
  pivot_wider(names_from = Generation, values_from = Population)
#> Warning: There was 1 warning in `mutate()`.
#> i In argument: `Age_num = as.numeric(Age_clean)`.
#> Caused by warning:
#> ! NAs introduits lors de la conversion automatique

reactable(
  df_demographic, 
  columns = list(
    Year = colDef(
      align = "center",  # Center align the year column
      minWidth = 100
    ),
    `Generation Z` = colDef(
      align = "right",  # Right align the population column
      style = function(value) {
        color <- ifelse(value > 860000, "green", "black")  # Green for population over 1 million
        list(color = color)
      }
    ),
    Millennials = colDef(
      align = "right",
      style = function(value) {
        color <- ifelse(value > 1900000, "green", "black")
        list(color = color)
      }
    ),
    `Generation X` = colDef(
      align = "right",
      style = function(value) {
        color <- ifelse(value > 2000000, "green", "black")
        list(color = color)
      }
    ),
    `Baby Boomers` = colDef(
      align = "right",
      style = function(value) {
        color <- ifelse(value > 2300000, "green", "black")
        list(color = color)
      }
    )
  ),
  highlight = TRUE,  # Highlight rows on hover
  defaultPageSize = 10,  # Display 10 rows per page
  paginationType = "numbers",  # Use numbers for page navigation
  searchable = TRUE,  # Make the table searchable
  sortable = TRUE,  # Allow sorting
  resizable = TRUE  # Allow column resizing
)

Code

str(df_demographic)
#> gropd_df [18 x 5] (S3: grouped_df/tbl_df/tbl/data.frame)
#>  $ Year        : Date[1:18], format: "2005-01-01" ...
#>  $ Generation Z: int [1:18] 788674 797102 803780 819198 835811 8482..
#>  $ Millennials : int [1:18] 1783064 1762564 1743133 1743763 1754815..
#>  $ Generation X: int [1:18] 1693753 1722516 1754075 1790725 1832671..
#>  $ Baby Boomers: int [1:18] 1764390 1800973 1839602 1876304 1914329..
#>  - attr(*, "groups")= tibble [18 x 2] (S3: tbl_df/tbl/data.frame)
#>   ..$ Year : Date[1:18], format: "2005-01-01" ...
#>   ..$ .rows: list<int> [1:18] 
#>   .. ..$ : int 1
#>   .. ..$ : int 2
#>   .. ..$ : int 3
#>   .. ..$ : int 4
#>   .. ..$ : int 5
#>   .. ..$ : int 6
#>   .. ..$ : int 7
#>   .. ..$ : int 8
#>   .. ..$ : int 9
#>   .. ..$ : int 10
#>   .. ..$ : int 11
#>   .. ..$ : int 12
#>   .. ..$ : int 13
#>   .. ..$ : int 14
#>   .. ..$ : int 15
#>   .. ..$ : int 16
#>   .. ..$ : int 17
#>   .. ..$ : int 18
#>   .. ..@ ptype: int(0) 
#>   ..- attr(*, ".drop")= logi TRUE

2.2.4 Data Wrangling : Swiss vehicle clean

Code

# Define a function to process vehicle data, excluding the first row as it's not actual data
process_vehicle_data <- function(vehicle_data, start_year, end_year) {
  # the first row is a header or metadata and should be excluded
  vehicle_data <- vehicle_data[-1, ]
  
  col_names <- c("Canton", "VehicleGroupType", "Fuel", "Month", paste0("X", start_year:end_year))
  names(vehicle_data) <- col_names
  
  vehicle_data %>%
    filter(!str_detect(trimws(VehicleGroupType), "^>")) %>%
    rename(Location = Canton, VehicleType = VehicleGroupType) %>%
    mutate(VehicleType = str_remove(VehicleType, "^\\.\\.\\.\\s*")) %>%
    pivot_longer(cols = all_of(paste0("X", start_year:end_year)), names_to = "Year", values_to = "Count") %>%
    mutate(Year = as.numeric(str_remove(Year, "X")),
           MonthNum = match(Month, month.name),
           Date = as.Date(paste(Year, ifelse(is.na(MonthNum), 1, MonthNum), "01", sep = "-"), format = "%Y-%m-%d")) %>%
    select(-Month, -Year, -MonthNum)
}

# Process 2005 to 2008 and 2009 to 2022 data
v_2005_2008 <- process_vehicle_data(vehicle_data_2005_2008, 2005, 2008)
v_2009_2022 <- process_vehicle_data(vehicle_data_2009_2022, 2009, 2022)

# Merge and arrange data
df_v <- bind_rows(v_2005_2008, v_2009_2022) %>%
  arrange(Date)

# Canton and Fuel Type Standardization
standard_names <- c(
  "Switzerland" = "Switzerland", "Zürich" = "ZH", "Bern" = "BE", "Luzern" = "LU",
  "Uri" = "UR", "Schwyz" = "SZ", "Obwalden" = "OW", "Nidwalden" = "NW",
  "Glarus" = "GL", "Zug" = "ZG", "Fribourg" = "FR", "Solothurn" = "SO",
  "Basel-Stadt" = "BS", "Basel-Landschaft" = "BL", "Schaffhausen" = "SH",
  "Appenzell Ausserrhoden" = "AR", "Appenzell Innerrhoden" = "AI", 
  "St. Gallen" = "SG", "Graubünden" = "GR", "Aargau" = "AG", 
  "Thurgau" = "TG", "Ticino" = "TI", "Vaud" = "VD", "Valais" = "VS",
  "Neuchâtel" = "NE", "Genève" = "GE", "Jura" = "JU", "Confederation" = "Confederation"
)

df_v <- df_v %>%
  mutate(Location = iconv(Location, from = "latin1", to = "UTF-8")) %>%
  mutate(Location = map_chr(str_split(Location, " / "), ~ .x[1])) %>%
  mutate(Location = standard_names[Location],
         Fuel = case_when(
           Fuel %in% c("Petrol-electricity: conventional hybrid", "Diesel-electricity: conventional hybrid") ~ "Conventional hybrid",
           Fuel %in% c("Petrol-electricity: plug-in hybrid", "Diesel-electricity: plug-in hybrid") ~ "Plug-in hybrid",
           Fuel == "Gas (monovalent and bivalent)" ~ "Gas",
           TRUE ~ Fuel))

# Count number of vehicles for a particular year
#vehicle_count_2022 <- df_v %>%
#  filter(Location == "Switzerland", year(Date) == 2023, VehicleType == "Passenger car") %>%
#  summarize(TotalCount = sum(Count))

Redundancy

The dataset, representing new vehicle registrations in Switzerland from 2005 onwards, exhibited redundancy in its structure. For example, the data contained both main categories (like ‘> Passenger cars’) and their respective sub-categories (‘… Passenger car’ and ‘.. Heavy passenger car’). On examination, it was observed that the counts under the main categories were simply aggregates of the counts of their sub-categories. Such redundancy could lead to double counting in analytical processes.

We addressed this issue by removing main categories that were aggregates of their sub-categories, preventing potential double counting in analyses. For two subsets of data (2005 to 2008 and 2009 to 2022), We filtered out main categories, cleaned and standardized the column names, reshaped the data set from wide to long format to facilitate analysis, and extracted and formatted the year information for each observation. This process allowed for a more granular and consistent data set for further analysis and interpretation.

Merging and Joining

As the vehicle data was split across multiple files, it was necessary to combine them for a holistic view.

We used bind_rowsto create df_v, then sorted the dataset by year to ensure a structured chronological view of the data.

Rows with 0

Retaining rows with a Count of 0 ensures the completeness of our dataset, indicating periods when specific vehicle types or fuel categories had no registrations. This not only provides a holistic view of vehicle adoption trends over time but also aids in generating continuous time series visualizations without gaps, offering a true representation of the data.

Code

# Display cleaned data
reactable(head(df_v, 500), sortable = TRUE, searchable = TRUE)

2.2.5 Data wrangling French vehicle clean

This dataset will be used to compare our data in Switzerland with a bordering country (FR)

It is important to note that this data set contains all registered vehicles in a given year (vs. all new registered cars for our Swiss data) We will only keep the vehicles used for personal use (not the ones for professional use), to match our Swiss dataset

We decided to remove some anecdotal observations (i.e Hydrogen, Unknown, etc.) as their production / use is marginal. Moreover, we have decided to regroup the different kind of hybrid motors into two Hybrid categories. The 2 categories are the plugable hybrid vehicle HR (petrol or diesel) and the non-plugable hybrid vehicles HNR (petrol or diesel). This was done to match our Swiss data set.

The main issue with this data set was to set it in a way where it is comparable to our Swiss cars data set. Indeed, this dataset presents the total of all cars in France for a given year and type, while our Swiss data set accounts for new registration of cars for a given year and type.

To be able to compare two data sets, we decided to format the French data set in the following way:

Remove categories that are not relevant for our analysis, and widely under-represented in the population (i.e Gaz, hydrogen)
Focus on the private use of cars (vs. professional), because the decision to use a certain type of cars for professional reasons are often explained by factors hard to capture in our analysis (i.e bundle deals, greenwashing). We therefore hypothesized that private use of a certain type of car better gives insights on population view.
The match our swiss data set (new registration per year), we computed the difference of total cars registered in France per years. Having only the data between 2011 and 2022, we have “lost” the insights for 2011. Indeed, when creating the deltas (difference) columns, 2011 deltas could not be computed (no data for 2010), we therefore decided to delete that year from our data

Code

# Only taking the private cars from this data and transforming the data
df_v_fr <- france_v[25:37, ] %>%
  t() %>%
  as_tibble() %>%
  setNames(.[1, ]) %>%
  slice(-1) %>%
  mutate(Year = 2011:2022) %>%
  select(-c("Particulier", "Gaz", "Gaz HNR", "Gaz HR", "Hydrogène et autre ZE", "Inconnu")) %>%
  mutate(across(-Year, ~ floor(as.numeric(.)))) %>%
  mutate(
    Conventional_Hybrid = as.numeric(`Diesel HNR`) + as.numeric(`Essence HNR`),
    Plug_in_Hybrid = as.numeric(`Diesel HR`) + as.numeric(`Essence HR`),
    across(c(Diesel, Essence, Conventional_Hybrid, Plug_in_Hybrid, Electrique), as.numeric),
    Diesel_delta = Diesel - lag(Diesel),
    Essence_delta = Essence - lag(Essence),
    Conventional_Hybrid_delta = Conventional_Hybrid - lag(Conventional_Hybrid),
    Plug_in_Hybrid_delta = Plug_in_Hybrid - lag(Plug_in_Hybrid),
    Electrique_delta = Electrique - lag(Electrique)
  ) %>%
  filter(!is.na(Diesel_delta)) %>%
  select(Date = Year, Diesel, Diesel_delta, Essence, Essence_delta, Conventional_Hybrid, Conventional_Hybrid_delta, Plug_in_Hybrid, Plug_in_Hybrid_delta, Electrique, Electrique_delta) %>%
  mutate(Date = as.Date(paste(Date, "-01-01", sep = ""), format = "%Y-%m-%d"))
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column
#> names if `.name_repair` is omitted as of tibble 2.0.0.
#> i Using compatibility `.name_repair`.

# Display cleaned data
reactable(head(df_v_fr, 100), sortable = TRUE, searchable = TRUE)

2.2.6 Availability of charging stations

This data set will be used to explore and analyze the effects of charging station availability on EVs adoption.

However, two important points are to note.

We were unable to find data before November 2020
Charging station availability and EV in market are likely strongly reciprocally correlated.

Moreover, it is important to keep standardized dates and locations

We merges ‘year’ and ‘month’ columns to create a ‘Date’ column in the format “YYYY-MM-01”, subsequently reorganizing the dataframe to position ‘Date’ as the first column and removing the original ‘year’ and ‘month’ columns.

Code

# Combine 'year' and 'month' columns to create a new 'Date' column
charging_station$Date <- as.Date(paste(charging_station$year, charging_station$month, "01", sep = "-"), format = "%Y-%m-%d")

# Rearrange columns with 'Date' as the first column and drop 'year' and 'month'
df_charging_station <- charging_station %>%
  select(Date, everything()) %>%
  select(-year, -month)

# Display cleaned data
reactable(df_charging_station,
          sortable = TRUE, 
          searchable = TRUE)

2.2.7 Availability of charging station in France and Switzerland

This data set is used to complement the prior charging station data set.

It has data going back to 2012. And data for both Switzerland and France

Code

# Only selecting necessary columns
charge_ch_fr <- charge_ch_fr %>%
  select(c("year", "region", "powertrain", "value"))

# Setting year as.Date
charge_ch_fr$year <- as.Date(
  paste(charge_ch_fr$year,"-01-01", sep = ""), format = "%Y-%m-%d")

df_charge_number_CH <- charge_ch_fr %>%
  filter(region == "Switzerland")

df_charge_number_FR <- charge_ch_fr %>%
  filter(region == "France")

2.2.8 Political strength per canton

This data set will be used to explore and analyze the effects of political convictions on EVs adoption (per Canton).

The 2 main difficulties with this data set were the following: 1. Create a process (loop) general enough to clean all the Cantons at the same time, but specific enough for it to work 2. The Swiss political parties’ names have evolved quite a bit over the years.

To use this data effectively in our analysis. We will group them in 5 categories.

The 5 categories respond to the following question: “What is the political party’s stance on sustainability / ecological measures?” 1. Against 2. Slightly Against 3. Neutral 4. Slightly in Favour 5. In Favour

We assigned each political parties in one of those categories based on their program.

Code

# We import each different sheets (one per canton) into a data set
political_data_sheets <- lapply(setdiff(excel_sheets("../data/political_data.xlsx"), "Contenu"), 
                                function(sheet) {
                                  p_data <- suppressMessages(read_excel("../data/political_data.xlsx", sheet = sheet))
                                  return(p_data)
                                })

sheet_names <- setdiff(political_data_sheets_prep, "Contenu")

named_data_list <- setNames(political_data_sheets, sheet_names)

# Now to the cleaning part
for (i in seq_along(named_data_list)) {
  # setting one dataset we work on
  current_dataset <- named_data_list[[i]]
  
  # where is "taux de participation"
  index_to_keep <- which(current_dataset[[1]] == "Taux de participation")[1]
  
  # keep only rows until "taux de participation" and delete the first one "force des partis"
  named_data_list[[i]] <- current_dataset[2:index_to_keep-2, ]
  
  # remove columns 2 and 3 (they are all NAs)
  named_data_list[[i]] <- named_data_list[[i]][ ,-c(2,3)]
  
  # transposing the data sets to have years in a single columns
  transposed_data <- t(named_data_list[[i]])
  named_data_list[[i]] <- as_tibble(transposed_data[-1, ])
  
  # set column names and change date as.Date
  colnames(named_data_list[[i]]) <- transposed_data[1, ]
  
  named_data_list[[i]][[2]] <- as.Date(
  paste(named_data_list[[i]][[2]], "-01-01", sep = ""), format = "%Y-%m-%d")
  
  # find the columns that are only NAs and remove them
  named_data_list[[i]] <- named_data_list[[i]][, colSums(!is.na(named_data_list[[i]])) > 0, drop = FALSE]
  
  # Removing all non-numeric values
  named_data_list[[i]][, -1] <- apply(named_data_list[[i]][, -1], 2, function(x) as.numeric(gsub("[^0-9.]", "", x)))
  
  # Setting the first column name as "Date"
  colnames(named_data_list[[i]])[1] <- "Date"
  
  # Removing the rows before 01.01.1999
  threshold_date <- as.Date("1999-01-01")
  named_data_list[[i]] <- named_data_list[[i]][named_data_list[[i]]$Date >= threshold_date, ]
}
#> Warning in FUN(newX[, i], ...): NAs introduits lors de la conversion
#> automatique

# Creating a map for the political parties stance on sustainability
sustainability_mapping <- c(
  "Against" = c("MCG (MCR)","PBD",  "PBD 1",    "PBD 2",    "UDC"),
  "Slightly Against" = c("Lega","PDC",  "PDC 1",    "PDC 2",    "PLR",  "PLS",  "UDF"),
  "Neutral" = c("Adl",  "DS",   "PdL",  "POCH", "PSL",  "PST",  "Rép.", "Sol.","Separ.", "Autres"),
  "Slightly in Favour" = c("Il Centro", "Il Centro 1",  "Le Centre 1","Le Centre",  "Le Centre 2",  "PCS","PPD",    "PPD 1"),
  "In Favour" = c("AVF",    "AVF 1",    "PEV", "PS",    "PSA",  "PVL","VERDI",  "VERDI 2","VERT-E-S  ", "VERT-E-S 2",   "VERT-E-S 3")
)

# Looping through the data sets the change the names of the parties by their stance
for (i in seq_along(named_data_list)){
  current_dataset <- named_data_list[[i]]
  
  for (party_name in names(current_dataset)[-1]) {
    stance <- sapply(sustainability_mapping, function(x) party_name %in% x)
    stance <- names(stance)[which(stance)]
    
    if (length(stance) > 0) {
      selected_columns <- intersect(c(party_name, stance), colnames(current_dataset))
      
      current_dataset[[stance]] <- rowSums(current_dataset[selected_columns], na.rm = TRUE)
      current_dataset[[party_name]] <- NULL
    }
  }
  named_data_list[[i]] <- current_dataset
}

# Creating my 5 columns
categories <- c("Against", "Slightly Against", "Neutral", "Slightly in Favour", "In Favour")

# Aggregating the information of the data sets into these 5 categories
for (i in seq_along(named_data_list)) {
  current_dataset <- named_data_list[[i]]
  
  # Extract the Date column
  result_dataset <- current_dataset[, "Date", drop = FALSE]
  
  # Loop through each category and aggregate values
  for (category in categories) {
    matching_columns <- grep(paste0("^", category, "\\d*$"), colnames(current_dataset), value = TRUE)
    result_dataset[[category]] <- rowSums(current_dataset[matching_columns], na.rm = TRUE)
  }
  
  named_data_list[[i]] <- result_dataset
}

# Storing the data sets into a list:
list_politic <- list()

for (i in seq_along(named_data_list)) {
  tibble_name <- paste0("politic_", sheet_names[i])
  list_politic[[tibble_name]] <- named_data_list[[i]]
}

# Correcting a NA in politic_AI
new_dates <- list_politic[["politic_VD"]]$Date
list_politic[["politic_AI"]]$Date <- new_dates

# Creating a data set per year for Switzerland
political_combined_data <- bind_rows(list_politic, .id = "Canton")
political_combined_data$Year <- as.integer(format(political_combined_data$Date, "%Y"))
political_combined_data <- political_combined_data[, -which(names(political_combined_data) == "Date")]

political_summarized_data <- political_combined_data %>%
  group_by(Year, Canton) %>%
  summarize(
    Against = sum(Against),
    `Slightly Against` = sum(`Slightly Against`),
    Neutral = sum(Neutral),
    `Slightly in Favour` = sum(`Slightly in Favour`),
    `In Favour` = sum(`In Favour`)
  )

yearly_political_datasets <- list()
unique_years <- unique(political_combined_data$Year)
for (year in unique_years) {
  year_political_dataset <- political_combined_data %>% filter(Year == year)
  yearly_political_datasets[[as.character(year)]] <- year_political_dataset
}

# now accessible via ' political_yearly_data$Year '
political_combined_data <- political_combined_data %>%
  mutate(
    Canton = sub("politic_", "", Canton),  # Remove 'politic_' prefix
    Year = ymd(paste(Year, "01", "01"))    # Convert Year to date type
  )

political_combined_data <- political_combined_data %>%
  mutate(
    KANTONSNUM = case_when(
      Canton == "ZH" ~ 1,
      Canton == "BE" ~ 2,
      Canton == "LU" ~ 3,
      Canton == "UR" ~ 4,
      Canton == "SZ" ~ 5,
      Canton == "OW" ~ 6,
      Canton == "NW" ~ 7,
      Canton == "GL" ~ 8,
      Canton == "ZG" ~ 9,
      Canton == "FR" ~ 10,
      Canton == "SO" ~ 11,
      Canton == "BS" ~ 12,
      Canton == "BL" ~ 13,
      Canton == "SH" ~ 14,
      Canton == "AR" ~ 15,
      Canton == "AI" ~ 16,
      Canton == "SG" ~ 17,
      Canton == "GR" ~ 18,
      Canton == "AG" ~ 19,
      Canton == "TG" ~ 20,
      Canton == "TI" ~ 21,
      Canton == "VD" ~ 22,
      Canton == "VS" ~ 23,
      Canton == "NE" ~ 24,
      Canton == "GE" ~ 25,
      Canton == "JU" ~ 26
    )
  )

# Display cleaned data
reactable(political_combined_data,
          sortable = TRUE, 
          searchable = TRUE)

Code

str(political_combined_data)
#> tibble [182 x 8] (S3: tbl_df/tbl/data.frame)
#>  $ Canton            : chr [1:182] "ZH" "ZH" "ZH" "ZH" ...
#>  $ Against           : num [1:182] 32.5 33.4 33.9 35.1 34.3 ...
#>  $ Slightly Against  : num [1:182] 24.9 23.7 22.8 18.8 21.6 ...
#>  $ Neutral           : num [1:182] 7.46 12.36 9.09 2.64 2.8 ...
#>  $ Slightly in Favour: num [1:182] 0.218 0 0.147 0.157 0 ...
#>  $ In Favour         : num [1:182] 35 39.7 42 43.2 41.3 ...
#>  $ Year              : Date[1:182], format: "1999-01-01" ...
#>  $ KANTONSNUM        : num [1:182] 1 1 1 1 1 1 1 2 2 2 ...

2.2.9 Swiss Population

We chose to use the population data from the year 2022 to ensure the most current and relevant demographic context, providing a contemporary snapshot that aligns closely with the latest trends in electric vehicle registrations.

Assuming the relevant data starts from row 5, canton names are in the first column, and the population figures for 2022 are in the second column.

Code

df_swisspop_2022 <- df_swisspop_2022 %>%
  slice(-1:-4) %>%  # Remove the first 4 rows
  select(Canton = 1, TotalPopulation = 2)  # Select only the canton names and population figures

# Remove rows with NAs in the Canton column
df_swisspop_2022 <- df_swisspop_2022 %>%
  filter(!is.na(Canton))

# Map Canton names to abbreviations
df_swisspop_2022 <- df_swisspop_2022 %>%
  mutate(CantonAbbreviation = case_when(
    Canton == "Zurich" ~ "ZH",
    Canton == "Bern" ~ "BE",
    Canton == "Lucerne" ~ "LU",
    Canton == "Uri" ~ "UR",
    Canton == "Schwyz" ~ "SZ",
    Canton == "Obwalden" ~ "OW",
    Canton == "Nidwalden" ~ "NW",
    Canton == "Glarus" ~ "GL",
    Canton == "Zug" ~ "ZG",
    Canton == "Fribourg" ~ "FR",
    Canton == "Solothurn" ~ "SO",
    Canton == "Basel-Stadt" ~ "BS",
    Canton == "Basel-Landschaft" ~ "BL",
    Canton == "Schaffhausen" ~ "SH",
    Canton == "Appenzell A. Rh." ~ "AR",
    Canton == "Appenzell I. Rh." ~ "AI",
    Canton == "St. Gallen" ~ "SG",
    Canton == "Graubünden" ~ "GR",
    Canton == "Aargau" ~ "AG",
    Canton == "Thurgau" ~ "TG",
    Canton == "Ticino" ~ "TI",
    Canton == "Vaud" ~ "VD",
    Canton == "Valais" ~ "VS",
    Canton == "Neuchâtel" ~ "NE",
    Canton == "Geneva" ~ "GE",
    Canton == "Jura" ~ "JU",
    TRUE ~ NA_character_  # For unrecognized cantons
  ))

# Map Canton names to KANTONSNUM
df_swisspop_2022 <- df_swisspop_2022 %>%
  mutate(KANTONSNUM = case_when(
    Canton == "Graubünden" ~ 18,
    Canton == "Bern" ~ 2,
    Canton == "Valais" ~ 23,
    Canton == "Vaud" ~ 22,
    Canton == "Ticino" ~ 21,
    Canton == "St. Gallen" ~ 17,
    Canton == "Zurich" ~ 1,
    Canton == "Fribourg" ~ 10,
    Canton == "Lucerne" ~ 3,
    Canton == "Aargau" ~ 19,
    Canton == "Uri" ~ 4,
    Canton == "Thurgau" ~ 20,
    Canton == "Schwyz" ~ 5,
    Canton == "Jura" ~ 26,
    Canton == "Neuchâtel" ~ 24,
    Canton == "Solothurn" ~ 11,
    Canton == "Glarus" ~ 8,
    Canton == "Basel-Landschaft" ~ 13,
    Canton == "Obwalden" ~ 6,
    Canton == "Nidwalden" ~ 7,
    Canton == "Geneva" ~ 25,
    Canton == "Schaffhausen" ~ 14,
    Canton == "Appenzell A. Rh." ~ 15,
    Canton == "Zug" ~ 9,
    Canton == "Appenzell I. Rh." ~ 16,
    Canton == "Basel-Stadt" ~ 12,
    TRUE ~ NA_integer_  # For unrecognized cantons
  ))


df_swisspop_2022$TotalPopulation <- as.integer(df_swisspop_2022$TotalPopulation)
# Filter out rows where CantonAbbreviation is NA
df_swisspop_2022 <- df_swisspop_2022 %>%
  filter(!is.na(CantonAbbreviation))

# Display cleaned data
reactable(df_swisspop_2022,
          sortable = TRUE, 
          searchable = TRUE)

Code

# Loop for 5 years of swisspop
years = c(2022, 2021, 2020, 2019, 2018)

for (year in years) {
  df_name <- paste0("df_swisspop_",year)
  df <- get(df_name)
  df <- df %>%
    slice(-1:-4) %>%
    select(Canton = 1, TotalPopulation = 2)

  # Remove rows with NAs in the Canton column
  df <- df %>%
    filter(!is.na(Canton))  
  
  # Map Canton names to abbreviations
  df <- df %>%
    mutate(CantonAbbreviation = case_when(
      Canton == "Zurich" ~ "ZH",
      Canton == "Bern" ~ "BE",
      Canton == "Lucerne" ~ "LU",
      Canton == "Uri" ~ "UR",
      Canton == "Schwyz" ~ "SZ",
      Canton == "Obwalden" ~ "OW",
      Canton == "Nidwalden" ~ "NW",
      Canton == "Glarus" ~ "GL",
      Canton == "Zug" ~ "ZG",
      Canton == "Fribourg" ~ "FR",
      Canton == "Solothurn" ~ "SO",
      Canton == "Basel-Stadt" ~ "BS",
      Canton == "Basel-Landschaft" ~ "BL",
      Canton == "Schaffhausen" ~ "SH",
      Canton == "Appenzell A. Rh." ~ "AR",
      Canton == "Appenzell I. Rh." ~ "AI",
      Canton == "St. Gallen" ~ "SG",
      Canton == "Graubünden" ~ "GR",
      Canton == "Aargau" ~ "AG",
      Canton == "Thurgau" ~ "TG",
      Canton == "Ticino" ~ "TI",
      Canton == "Vaud" ~ "VD",
      Canton == "Valais" ~ "VS",
      Canton == "Neuchâtel" ~ "NE",
      Canton == "Geneva" ~ "GE",
      Canton == "Jura" ~ "JU",
      TRUE ~ NA_character_  # For unrecognized cantons
    ))
  
  # Map Canton names to KANTONSNUM
  df <- df %>%
    mutate(KANTONSNUM = case_when(
      Canton == "Graubünden" ~ 18,
      Canton == "Bern" ~ 2,
      Canton == "Valais" ~ 23,
      Canton == "Vaud" ~ 22,
      Canton == "Ticino" ~ 21,
      Canton == "St. Gallen" ~ 17,
      Canton == "Zurich" ~ 1,
      Canton == "Fribourg" ~ 10,
      Canton == "Lucerne" ~ 3,
      Canton == "Aargau" ~ 19,
      Canton == "Uri" ~ 4,
      Canton == "Thurgau" ~ 20,
      Canton == "Schwyz" ~ 5,
      Canton == "Jura" ~ 26,
      Canton == "Neuchâtel" ~ 24,
      Canton == "Solothurn" ~ 11,
      Canton == "Glarus" ~ 8,
      Canton == "Basel-Landschaft" ~ 13,
      Canton == "Obwalden" ~ 6,
      Canton == "Nidwalden" ~ 7,
      Canton == "Geneva" ~ 25,
      Canton == "Schaffhausen" ~ 14,
      Canton == "Appenzell A. Rh." ~ 15,
      Canton == "Zug" ~ 9,
      Canton == "Appenzell I. Rh." ~ 16,
      Canton == "Basel-Stadt" ~ 12,
      TRUE ~ NA_integer_  # For unrecognized cantons
    ))
  
  # Convert TotalPopulation to integer
  df$TotalPopulation <- as.integer(df$TotalPopulation)
  
  # Filter out rows where CantonAbbreviation is NA
  df <- df %>%
    filter(!is.na(CantonAbbreviation))
  
  assign(df_name, df)
}

Code

str(df_demographic)
#> gropd_df [18 x 5] (S3: grouped_df/tbl_df/tbl/data.frame)
#>  $ Year        : Date[1:18], format: "2005-01-01" ...
#>  $ Generation Z: int [1:18] 788674 797102 803780 819198 835811 8482..
#>  $ Millennials : int [1:18] 1783064 1762564 1743133 1743763 1754815..
#>  $ Generation X: int [1:18] 1693753 1722516 1754075 1790725 1832671..
#>  $ Baby Boomers: int [1:18] 1764390 1800973 1839602 1876304 1914329..
#>  - attr(*, "groups")= tibble [18 x 2] (S3: tbl_df/tbl/data.frame)
#>   ..$ Year : Date[1:18], format: "2005-01-01" ...
#>   ..$ .rows: list<int> [1:18] 
#>   .. ..$ : int 1
#>   .. ..$ : int 2
#>   .. ..$ : int 3
#>   .. ..$ : int 4
#>   .. ..$ : int 5
#>   .. ..$ : int 6
#>   .. ..$ : int 7
#>   .. ..$ : int 8
#>   .. ..$ : int 9
#>   .. ..$ : int 10
#>   .. ..$ : int 11
#>   .. ..$ : int 12
#>   .. ..$ : int 13
#>   .. ..$ : int 14
#>   .. ..$ : int 15
#>   .. ..$ : int 16
#>   .. ..$ : int 17
#>   .. ..$ : int 18
#>   .. ..@ ptype: int(0) 
#>   ..- attr(*, ".drop")= logi TRUE
str(df_oil)
#> 'data.frame':    4527 obs. of  2 variables:
#>  $ Date : Date, format: "2005-01-04" ...
#>  $ Price: num  40.8 41 43.2 43.3 44.7 ...
str(political_combined_data)
#> tibble [182 x 8] (S3: tbl_df/tbl/data.frame)
#>  $ Canton            : chr [1:182] "ZH" "ZH" "ZH" "ZH" ...
#>  $ Against           : num [1:182] 32.5 33.4 33.9 35.1 34.3 ...
#>  $ Slightly Against  : num [1:182] 24.9 23.7 22.8 18.8 21.6 ...
#>  $ Neutral           : num [1:182] 7.46 12.36 9.09 2.64 2.8 ...
#>  $ Slightly in Favour: num [1:182] 0.218 0 0.147 0.157 0 ...
#>  $ In Favour         : num [1:182] 35 39.7 42 43.2 41.3 ...
#>  $ Year              : Date[1:182], format: "1999-01-01" ...
#>  $ KANTONSNUM        : num [1:182] 1 1 1 1 1 1 1 2 2 2 ...

3 Exploratory data analysis

3.1 Switzerland

3.1.1 seasonality

Code

#creating dataset for the three seasonality graphs
passenger_cars_processed <- df_v %>%
  filter(VehicleType == "Passenger car") %>%
  mutate(YearMonth = floor_date(Date, "month")) %>%
  group_by(YearMonth) %>%
  summarise(Count = sum(Count, na.rm = TRUE)) %>%
  ungroup() %>%
  mutate(Year = year(YearMonth), 
         Month = factor(month(YearMonth), levels = 1:12, labels = month.abb)) %>%
  arrange(Year, Month)

# plot1
p_seaso_1 <- ggplot(passenger_cars_processed, aes(x = YearMonth, y = Count)) +
  geom_line(color = "darkblue", size = 0.5) +
  labs(title = "Passenger Car Adoption Over Time in Switzerland",
       x = "Date",
       y = "Number of Passenger Cars Registered") +
  theme_minimal() +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  geom_smooth(method = "loess", se = FALSE, color = "blue") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> i Please use `linewidth` instead.

# Convert to an interactive plotly object
interactive_plot_seaso_1 <- ggplotly(p_seaso_1, width = 600, height = 400)

# Display the interactive plot
interactive_plot_seaso_1

The blue line represents a smoothed trend, indicating an initial increase in car registrations until around 2014-2015, followed by a gradual decline. The black line shows the actual number of cars registered, with significant variability. We will call this variation seasonality which is better represented in the following graph.

Code

# Plotting the data with ggplot2, showing the trend within each year
p_seaso_2 <- ggplot(passenger_cars_processed, aes(x = Month, y = Count, group = Year, color = as.factor(Year))) +
  geom_smooth(se = FALSE, method = "loess", span = 0.5, size = 0.7) +
  labs(title = "Monthly Passenger Car Registrations by Year",
       x = "Month",
       y = "Number of Passenger Cars Registered",
       color = "Year") +
  theme_minimal() +
  scale_color_viridis_d() +
  theme(legend.position = "bottom", axis.text.x = element_text(angle = 45, hjust = 1))

# Convert to an interactive plotly object
interactive_plot_seaso_2 <- ggplotly(p_seaso_2, width = 600, height = 400)

# Adjust plotly settings if needed, such as margins or layout
interactive_plot_seaso_2 <- interactive_plot_seaso_2 %>%
  layout(margin = list(l = 40, r = 10, b = 40, t = 40), # Adjust margins
         legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2)) # Adjust legend position

# Display the interactive plot
interactive_plot_seaso_2

This pattern suggests a seasonal trend with a mid-year peak and a year-end increase. 2020 reacts differently than other years. We suggest that it is probably related to Covid policies.

Code

# Plotting the data with ggplot2, showing the trend within each year
p_seaso_3 <- ggplot(passenger_cars_processed, aes(x = Month, y = Count, group = Year, color = as.factor(Year))) +
  geom_line() +
  facet_wrap(~ Year, scales = "free_y") +  # Facet by year with free y scales
  labs(title = "Seasonal Trends in Passenger Car Registrations",
       x = "Month",
       y = "Number of Passenger Cars Registered") +
  theme_minimal() +
  scale_color_viridis_d(guide = FALSE) +  # Use viridis color scale and remove the guide/legend
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + # Rotate x-axis labels by 45 degrees
  theme(axis.text.x = element_blank(), # This will remove the month labels
        axis.text.y = element_blank(), # This will remove the month labels
        axis.ticks.x = element_blank(), # This will remove the ticks on the x-axis
        legend.position = "none") # Remove the legend to clean up the plot

# Convert to an interactive plotly object
interactive_plot_seaso_3 <- ggplotly(p_seaso_3, width = 600, height = 400) %>%
  layout(xaxis = list(tickmode = "array",
                      tickvals = 1:12,
                      ticktext = month.abb))

# Display the interactive plot
interactive_plot_seaso_3

This version of the graphs helps us visualizing the evolution of registration for each year individually. As mentioned above, 2020 is the only year which show a decreasing peak instead of an increasing one as it is presented for all other years.

3.1.2 Vehicule Registration by Fuel time over time

Code

# Filter df_v for specific fuel types and vehicle type
filtered_df <- df_v %>%
  filter(Fuel %in% c("Petrol", "Diesel", "Conventional hybrid", "Plug-in hybrid", "Electricity") &
         VehicleType == "Passenger car")

# Group by Date and Fuel type, and summarize the count
fuel_type_trends <- filtered_df %>%
  group_by(Date, Fuel) %>%
  summarize(Count = sum(Count, na.rm = TRUE), .groups = 'drop')

# Plotting the trends over time by fuel type
p_fuel_over_time <- ggplot(fuel_type_trends, aes(x = Date, y = Count, color = Fuel)) +
  geom_line(alpha = 0.3) +  # Plot the actual lines
  geom_smooth(aes(group = Fuel), se = FALSE, method = "loess", span = 0.1) +  # Add smoothed lines per fuel type
  labs(title = "Vehicle Registrations by Fuel Type Over Time",
       x = "Date",
       y = "Number of Vehicles Registered") +
  theme_minimal() +
  theme(legend.position = "bottom")  # Adjust legend position to the bottom

# Convert to an interactive plotly object
interactive_plot_fuel_over_time <- ggplotly(p_fuel_over_time, width = 600, height = 400)
# Adjust plotly settings 
interactive_plot_fuel_over_time <- interactive_plot_fuel_over_time %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))

# Display the interactive plot
interactive_plot_fuel_over_time

The graph above shows the vehicle registrations in Switzerland by fuel type from 2005 to 2023.

On the one hand, it highlights the recent decrease in new registrations of thermic vehicles since 2017. On the other hand, the three EVs named as Conventional and Plug-in hybrids along with electricity ones are gradually increasing since approximately the same period. (In fact, fully electric cars and Conventional hybrids have even reached a higher level of vehicle registration in the past years, spotlighting the EV tendency in recent years.)

3.1.3 Availability of Charging station

3.1.3.1 Availability of Charging station in Switzerland

Code

p_charging_station <-ggplot(df_charge_number_CH, aes(x = year, y = value, group = powertrain, color = powertrain)) +
  geom_line() +
  labs(title = "Available charging station in Switzerland over the years",
       x = "Year",
       y = "Number of Charging Stations") +
  theme_minimal()

# Convert to an interactive plotly object
interactive_plot_charging_station <- ggplotly(p_charging_station, width = 600, height = 400)
# Adjust plotly settings 
interactive_plot_charging_station <- interactive_plot_charging_station %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))

# Display the interactive plot
interactive_plot_charging_station

3.1.3.2 Availability of Charging station in France

Code

p_charging_station_fr <-ggplot(df_charge_number_FR, aes(x = year, y = value, group = powertrain, color = powertrain)) +
  geom_line() +
  labs(title = "Available charging station in France over the years",
       x = "Year",
       y = "Number of Charging Stations") +
  theme_minimal()

# Convert to an interactive plotly object
interactive_plot_charging_station_fr <- ggplotly(p_charging_station_fr, width = 600, height = 400)
# Adjust plotly settings 
interactive_plot_charging_station_fr <- interactive_plot_charging_station_fr %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))

# Display the interactive plot
interactive_plot_charging_station_fr

3.1.4 Map

3.1.4.1 Count of Electricity car Registration for all years per cantons

swissBOUNDARIES3D_1_4_TLM_KANTONSGEBIET.shp contains the boundaries of the cantons of Switzerland.

The choice to display the sum of electric vehicle registrations over all years on the map was made to provide a comprehensive historical perspective, highlighting the total adoption of electric vehicles in each Swiss canton since the beginning of the dataset’s timeframe. This approach effectively captures the cumulative impact of electric vehicle adoption across the country.

The process involved filtering vehicle registration data for electric passenger cars and summing up the total registrations for each Swiss canton. This data was then combined with population figures and geographic boundaries to calculate electric vehicle (EV) registrations per capita, providing a standardized comparison of EV adoption across cantons.

Code

# Read in the shapefile for Swiss cantons
swiss_cantons <- st_read("../data/CH_map/swissBOUNDARIES3D_1_4_TLM_KANTONSGEBIET.shp")
#> Reading layer `swissBOUNDARIES3D_1_4_TLM_KANTONSGEBIET' from data source `C:\Users\troen\OneDrive\Bureau\HEC\Data Sciences for Business Analytics\dsfba_project-main\data\CH_map\swissBOUNDARIES3D_1_4_TLM_KANTONSGEBIET.shp' 
#>   using driver `ESRI Shapefile'
#> Simple feature collection with 50 features and 20 fields
#> Geometry type: POLYGON
#> Dimension:     XYZ
#> Bounding box:  xmin: 2490000 ymin: 1080000 xmax: 2830000 ymax: 1300000
#> z_range:       zmin: 193 zmax: 4610
#> Projected CRS: CH1903+ / LV95 + LN02 height
# Define canton abbreviations for matching
abbreviation_values <- c("ZH", "BE", "LU", "UR", "SZ", "OW", "NW", "GL", "ZG", "FR", "SO", "BS", "BL", "SH", "AR", "AI", "SG", "GR", "AG", "TG", "TI", "VD", "VS", "NE", "GE", "JU")

# Prepare the EV data with sum over all years
df_v_map <- df_v %>%
  filter(!Location %in% c("Switzerland", "Confederation"), 
         Fuel == "Electricity", VehicleType == "Passenger car") %>%
  mutate(KANTONSNUM = match(Location, abbreviation_values)) %>%
  group_by(KANTONSNUM) %>%
  summarize(TotalEV = sum(Count), .groups = 'drop')

# Merge EV data with population data
df_v_map <- left_join(df_v_map, df_swisspop_2022, by = c("KANTONSNUM" = "KANTONSNUM"))

str(df_v_map)
#> tibble [26 x 5] (S3: tbl_df/tbl/data.frame)
#>  $ KANTONSNUM        : num [1:26] 1 2 3 4 5 6 7 8 9 10 ...
#>  $ TotalEV           : int [1:26] 29541 11652 5365 325 2960 551 690..
#>  $ Canton            : chr [1:26] "Zurich" NA "Lucerne" "Uri" ...
#>  $ TotalPopulation   : int [1:26] 1579967 NA 424851 37317 164920 38..
#>  $ CantonAbbreviation: chr [1:26] "ZH" NA "LU" "UR" ...
# Calculate EV registrations per capita
df_v_map <- df_v_map %>%
  mutate(EV_per_Capita = TotalEV / TotalPopulation)

# Merge with shapefile data
map_data <- left_join(swiss_cantons, df_v_map, by = "KANTONSNUM")

# Ensure the geometries are valid and the CRS is set to WGS 84
# Check if 'map_data' is already an sf object
if (!inherits(map_data, "sf")) {
  map_data_sf <- st_as_sf(map_data, wkt = "geometry")
} else {
  map_data_sf <- map_data
}

# Ensure the geometries are valid and the CRS is set
map_data_sf <- st_make_valid(map_data_sf)
# Reproject the data to WGS 84 (EPSG:4326)
map_data_sf <- st_transform(map_data_sf, crs = 4326)


# Create color palettes for the 'Total' and 'EV_per_Capita' columns
color_palette_total <- colorNumeric(palette = "viridis", domain = map_data_sf$TotalEV)
color_palette_per_capita <- colorNumeric(palette = "viridis", domain = map_data_sf$EV_per_Capita)

# Create the leaflet maps
leaflet_map_total <- leaflet(map_data_sf) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(
    fillColor = ~color_palette_total(TotalEV),
    weight = 1,
    color = "#FFFFFF",
    fillOpacity = 0.7,
    popup = ~paste(NAME, "<br>Total EV Registrations: ", TotalEV)
  ) %>%
  addLegend(
    pal = color_palette_total, 
    values = ~TotalEV, 
    opacity = 0.7, 
    title = "Total EV <br> Registrations",
    position = "topright"
  )

leaflet_map_per_capita <- leaflet(map_data_sf) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(
    fillColor = ~color_palette_per_capita(EV_per_Capita),
    weight = 1,
    color = "#FFFFFF",
    fillOpacity = 0.7,
    popup = ~paste(NAME, "<br>EV Registrations per Capita: ", 
                   round(EV_per_Capita, 3))
  ) %>%
  addLegend(
    pal = color_palette_per_capita, 
    values = ~EV_per_Capita, 
    opacity = 0.7, 
    title = "EV Registrations <br> per Capita",
    position = "topright"
  )

# Print the maps to view them
leaflet_map_total

3.1.4.2 Count of Electricity car Registration for all years per cantons Standardized

For example, Zurich (ZH) has a relatively lower EV_per_Capita value (0.01870) despite a high total number of EV registrations (29,541), due to its large population (1,579,967). In contrast, Zug (ZG) shows a higher EV_per_Capita (0.04666) with fewer EV registrations (6,120) but a much smaller population (131,164), indicating a greater adoption rate when adjusted for population size.

The limitation of this approach is that it considers the total population, not accounting for the segment of the population that is of driving age or interested in vehicle ownership, which could further refine the EV adoption rates.

Code

leaflet_map_per_capita

3.2 Google Trend

Code

ggplot(df_gtrends, aes(x = Date, y = SearchRatio)) +
  geom_line(color = "darkgreen", size = 1, stat='smooth', se = FALSE, method = "loess", span = 0.1, size = 1) +
  labs(x = "Date", y = "Google Search", title = "Google search About EV in Switzerland")
#> Warning: Duplicated aesthetics after name standardisation: size

Another interesting point to look out is the Google search about EV engine in Switzerland. As we can see, it seems to have skyrocketed since 2016. This allows us to get an overview of the Swiss population interest concerning this topic and comfort us towards our initial predictions.

3.3 Oil

The chart below represents the oil price evolution through the last two decades. We can observe a quite high degree of volatility concerning the oil valuation. Nevertheless, it seems important to highlight that this value has greatly increased since the past 3 years.

The fig.show='animate' option tells Quarto to render the plot as an animation.

Code

## basic plot
#ggplot(df_oil, aes(x = Date , y = Price)) +
#  geom_line(color = "darkred", size = 1) +
#  labs(x = "Date", y = "Price", title = "Oil Price Over Time")

# Create a ggplot object with your data
p <- ggplot(df_oil, aes(x = Date, y = Price, group = 1)) +
  geom_line(color = "darkred", size = 1) +
  labs(x = "Date", y = "Price", title = "Oil Price Over Time")

# Animate the plot with gganimate, revealing the line over time
animated_plot <- p +
  transition_reveal(Date)

# Render the animation
animate(animated_plot, renderer = gganimate::gifski_renderer(), width = 600, height = 400, res = 96)

3.4 Demographics

Code

demographic_data_long <- df_demographic %>%
  pivot_longer(
    cols = c('Generation Z', 'Millennials', 'Generation X', 'Baby Boomers'),
    names_to = "Generation",
    values_to = "Population"
  )

# Plotting the data with ggplot2
p_demog <- ggplot(demographic_data_long, aes(x = Year, y = Population, color = Generation)) +
  geom_line(size = 1) +
  labs(title = "Demographic Trends in Switzerland",
       x = "Year",
       y = "Population") +
  theme_minimal() +
  theme(legend.position = "bottom") +
  scale_color_manual(values = c("Generation Z" = "blue", "Millennials" = "red", "Generation X" = "green", "Baby Boomers" = "purple"))

# Convert the ggplot object to an interactive plotly object
interactive_plot_demog <- ggplotly(p_demog, width = 600, height = 400)

# Adjust plotly settings 
interactive_plot_demog <- interactive_plot_demog%>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))

# The interactive plot can be displayed in an R Markdown document or a Shiny app
interactive_plot_demog

This graph concentrates on the demographic trends’ evolution for each segment of age above mentioned throughout the time. It points out the important overall rise of individuals living in Switzerland over the past two decades.

3.5 French vehicles

3.5.1 Total vehicles evolution France

Code

# Reshape data to long format
long_registration_data <- df_v_fr %>%
  select(Date, Diesel, Essence, Conventional_Hybrid, Plug_in_Hybrid, Electrique) %>%
  gather(key = "Fuel_Type", value = "Count", -Date)

# Create ggplot
p <- ggplot(long_registration_data, aes(x = Date, y = log(Count), color = Fuel_Type)) +
  geom_line() +
  scale_color_manual(values = c("Diesel" = "red", "Essence" = "blue", 
                                "Conventional_Hybrid" = "green", "Plug_in_Hybrid" = "purple", "Electrique" = "orange")) +
  labs(x = "Date", y = "Log-Scale Count", color = "Fuel Type") +
  theme_minimal()

# Convert to interactive plot and adjust legend
ggplotly(p, width = 600, height = 400) %>%
  layout(legend = list(orientation = 'h', x = 0.5, xanchor = 'center', y = -0.15))

3.5.2 Deltas evolution

Code

# Reshape data to long format
long_df_v_fr <- df_v_fr %>%
  select(Date, Diesel_delta, Essence_delta, Conventional_Hybrid_delta, Plug_in_Hybrid_delta, Electrique_delta) %>%
  gather(key = "Fuel_Type", value = "Delta", -Date)

# Map Fuel_Type values to desired names
long_df_v_fr$Fuel_Type <- recode(long_df_v_fr$Fuel_Type,
  "Diesel_delta" = "Diesel",
  "Essence_delta" = "Petrol",
  "Conventional_Hybrid_delta" = "Conventional hybrid",
  "Plug_in_Hybrid_delta" = "Plug-in hybrid",
  "Electrique_delta" = "Electricity"
)

# Create ggplot
p <- ggplot(long_df_v_fr, aes(x = Date, y = Delta, color = Fuel_Type)) +
  geom_line(size = 1) +
  labs(title = "Evolution of cars registered in France over the years by fuel type (Deltas)",
       x = "Years",
       y = "Value",
       color = "Fuel Category") +
  theme_minimal() +
  scale_color_manual(values = c("Diesel" = "red", "Petrol" = "blue", 
                                "Conventional hybrid" = "green", "Plug-in hybrid" = "purple",
                                "Electricity" = "orange"))

# Convert to interactive plot and adjust legend
ggplotly(p, width = 600, height = 400) %>%
  layout(legend = list(orientation = 'h', x = 0.5, xanchor = 'center', y = -0.3))

3.6 Political Parties

Here, we have a cluster plot of the Swiss Cantons according to their political stance on sustainability in 1999 and in 2023

Code

# Let's start with 1999
political_data_1999 <- political_combined_data[,-8] %>%
  filter(Year == as.Date("1999-01-01")) %>%
  select(c("Canton", "Against", "Slightly Against", "Neutral", "Slightly in Favour", "In Favour"))

# We will use the K-Means method
# We start by looking for the right amount of clusters

fviz_nbclust(political_data_1999[,-1], kmeans, method = "wss") +
  geom_vline(xintercept = 7, linetype = 2)

# We can see that 7 clusters seems to be the choice

# Changing my tibble
pol_cantons_1999 <- as.data.frame(political_data_1999)
rownames(pol_cantons_1999) <- pol_cantons_1999$Canton
pol_cantons_1999 <- pol_cantons_1999[,-1]

# Fit k-means with 7 clusters
km.res <- kmeans(pol_cantons_1999, 5, nstart = 26)

fviz_cluster(km.res, data = pol_cantons_1999) +
  ggtitle("Cluster Analysis of Cantons' stance on sustainability 1999")


# Then the PCA:
pca_cantons <- prcomp(pol_cantons_1999)
pca_plot <- fviz_pca(pca_cantons,
                     col.ind = as.factor(km.res$cluster),
                     palette = "jco",
                     label = "all",
                     repel = TRUE) +
  ggtitle("PCA Plot for each Canton")

# Adjust zoom level by setting xlim and ylim
pca_plot + coord_cartesian(xlim = c(-100, 100), ylim = c(-100, 100))

#Now the PCA for the variables
pca_variables <- prcomp(t(pol_cantons_1999))

# Plot variables
fviz_pca_var(pca_variables, col.var = "contrib", 
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE) +
  ggtitle("PCA Plot for Variables")

Code

# Now for 2023
political_data_2023 <- political_combined_data[,-8] %>%
  filter(Year == as.Date("2023-01-01")) %>%
  select(c("Canton", "Against", "Slightly Against", "Neutral", "Slightly in Favour", "In Favour"))

# Changing my tibble
pol_canton_2023 <- as.data.frame(political_data_2023)
row.names(pol_canton_2023) <- pol_canton_2023$Canton
pol_canton_2023 <- pol_canton_2023[,-1]

# How many clusters?
fviz_nbclust(pol_canton_2023, kmeans, method = "wss") +
  geom_vline(xintercept = 6, linetype = 2)

# We can see that 6 clusters seems to be the choice

# Fit k-means with 6 clusters
km.res_2023 <- kmeans(pol_canton_2023, 6, nstart = 26)
fviz_cluster(km.res_2023, data = pol_canton_2023) +
  ggtitle("Cluster Analysis of Cantons' stance on sustainability 2023")

#Then the PCA plot
pca_cantons <- prcomp(pol_canton_2023)
pca_plot <- fviz_pca(pca_cantons,
                     col.ind = as.factor(km.res$cluster),
                     palette = "jco",
                     label = "all",
                     repel = TRUE) +
  ggtitle("PCA Plot for each Canton")

# Adjust zoom level by setting xlim and ylim
pca_plot + coord_cartesian(xlim = c(-100, 100), ylim = c(-100, 100))

#Now the PCA for the variables
pca_variables <- prcomp(t(pol_canton_2023))

# Plot variables
fviz_pca_var(pca_variables, col.var = "contrib", 
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE) +
  ggtitle("PCA Plot for Variables")

3.7 Swiss vs France

3.7.1 Electric vs Hybrid vs Petrol

Here we compare and visualize specific vehicle fuel types between Swiss and French datasets.

The thicker line are the for Switzerland, the others are for France. We standardized the counts in both the Swiss and French datas ets for comparison purposes. Please juggle with the interactive plot to make it more readable.

Code

# Filtering Swiss data for specific fuel types
swiss_specific_fuel <- df_v %>%
  filter(Fuel %in% c("Diesel", "Electricity", "Conventional hybrid", "Plug-in hybrid", "Petrol")) %>%
  filter(Location == 'Switzerland') |>
  filter(VehicleType == 'Passenger car') |>
  filter(Date > as.Date('2012-01-01')) |>
  filter(Date < as.Date('2021-12-31'))

# Selecting equivalent columns from the French dataset
french_specific_fuel <- df_v_fr %>%
  select(Date, Diesel_delta, Essence_delta, Conventional_Hybrid_delta, Plug_in_Hybrid_delta, Electrique_delta) # Adjust column names accordingly

# Reshape French dataset to long format for easier plotting
french_specific_fuel_long <- french_specific_fuel %>%
  pivot_longer(cols = -Date, names_to = "Fuel", values_to = "Count")

# Standardize counts in each dataset
swiss_specific_fuel <- swiss_specific_fuel %>%
  mutate(Count = scale(Count))

french_specific_fuel_long <- french_specific_fuel_long %>%
  mutate(Count = scale(Count))

# Rename the 'Fuel' column in the French dataset
french_specific_fuel_long <- french_specific_fuel_long %>%
  mutate(Fuel = case_when(
    Fuel == "Diesel_delta" ~ "Diesel",
    Fuel == "Essence_delta" ~ "Petrol",
    Fuel == "Conventional_Hybrid_delta" ~ "Conventional hybrid",
    Fuel == "Plug_in_Hybrid_delta" ~ "Plug-in hybrid",
    Fuel == "Electrique_delta" ~ "Electricity"
  ))

# Define color palette for each fuel type
fuel_colors <- c("Diesel" = "black", "Electricity" = "green", "Conventional hybrid" = "purple", "Plug-in hybrid" = "blue", "Petrol" = "orange")

p <- ggplot() +
  geom_smooth(data = swiss_specific_fuel, aes(x = Date, y = Count, color = Fuel), 
              method = "loess", se = FALSE, size = 1.5) +
  geom_line(data = french_specific_fuel_long, aes(x = Date, y = Count, color = Fuel), 
            alpha = 0.4, size = 0.8) +
  scale_color_manual(values = fuel_colors, 
                     labels = c("Diesel", "Electricity", "Conventional hybrid", 
                                "Plug-in hybrid", "Petrol"),
                     breaks = c("Diesel", "Electricity", "Conventional hybrid", 
                                "Plug-in hybrid", "Petrol")) +
  labs(x = "Date", y = "Standardized Count", color = "Fuel Type") +
  theme_minimal() +
  geom_text(data = data.frame(x = as.Date("2021-01-01"), y = c(3, 2.8), 
                              label = c("Switzerland has", "the thickest line")), 
            aes(x = x, y = y, label = label, color = label), 
            size = 4, show.legend = FALSE)

interactive_plot <- ggplotly(p, width = 600, height = 400, tooltip = c("x", "y", "color"))
interactive_plot <- interactive_plot %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))
interactive_plot

Code

# Plotting with faceting and improved axis text
p <- ggplot() +
  geom_smooth(data = swiss_specific_fuel, aes(x = Date, y = Count, color = Fuel), 
              method = "loess", se = FALSE, size = 1.5) +
  geom_line(data = french_specific_fuel_long, aes(x = Date, y = Count, color = Fuel), 
            alpha = 0.4, size = 0.8) +
  scale_color_manual(values = fuel_colors, labels = c("Switzerland", "France")) +
  labs(x = "Date", y = "Standardized Count") +
  theme_minimal() +
  theme(legend.position = "bottom",
        strip.background = element_blank(),
        strip.text.x = element_text(size = 10, angle = 0),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        axis.title.x = element_text(size = 12, margin = margin(t = 10)),
        axis.title.y = element_text(size = 12, margin = margin(r = 10))) +
  facet_wrap(~Fuel, scales = 'free_y', ncol = 1)

# Convert to interactive plot
interactive_plot <- ggplotly(p, width = 600, height = 600, tooltip = c("x", "y", "color"))
interactive_plot <- interactive_plot %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))
interactive_plot

3.8 EV and Google Trends

All in all, the Google trend seems to be in adequation with the actual rise in EVs around Switzerland.

Code


df_v_electric <- df_v |>
  filter(VehicleType == "Passenger car") |>
  filter(Fuel == 'Electricity')
df_electric_vehicles_agg <- df_v_electric %>% group_by(Date) %>% summarize(Count = sum(Count))
# Resample both datasets to a monthly frequency using the first day of the month

# Merge datasets
merged_df <- merge(df_electric_vehicles_agg, df_gtrends, by = "Date")

# Calculate the ratio for the secondary axis
max_values <- merged_df |> summarize(max_count = max(Count, na.rm = TRUE), max_search = max(SearchRatio, na.rm = TRUE))
ratio <- max_values$max_count / max_values$max_search

# Add the ratio-adjusted SearchRatio to the merged dataset
merged_df <- merged_df |> mutate(AdjustedSearchRatio = SearchRatio * ratio)

# Plotting with smoothing and color changes
p <- ggplot(merged_df, aes(x = Date)) +
  geom_bar(aes(y = AdjustedSearchRatio, fill = "Google Trends"), stat = "identity") +
  geom_smooth(aes(y = Count, color = "Electric Vehicles Smoothed"), method = "loess", span = 0.2) +
  scale_y_continuous(
    "Number of Electric Vehicles",
    sec.axis = sec_axis(~ . / ratio, name = "Google Trends")
  ) +
  labs(title = "Comparison of Electric Vehicle Rise and Google Trends Over Time",
       x = "Date", fill = "Legend", color = "Legend") +
  scale_fill_manual(values = c("Google Trends" = "darkred")) +
  scale_color_manual(values = c("Electric Vehicles Smoothed" = "blue")) +
  theme_minimal() +
  theme(legend.position = "bottom")

# Convert to interactive plot
interactive_plot <- ggplotly(p, width = 600, height = 400) %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.3))

interactive_plot

3.9 EV and Oil Price

The results of the graphic comparing oil price and EVs evolution comforts us in our choice of oil price as an explicative variable. Indeed, the tendency is quite similar since 2020. In fact, the previous years refer to a period where EVs were not as commercialized as today. Obviously, we are aware of the numerous other variables explaining both oil price and EVs rise over time.

Code

# Resample data to monthly frequency and calculate mean oil price
df_oil_monthly <- df_oil %>% 
                  mutate(Date = as.Date(format(Date, "%Y-%m-01"))) %>%
                  group_by(Date) %>% 
                  summarize(Price = mean(Price), .groups = 'drop')

# Resample electric vehicles data to monthly frequency and sum counts
df_electric_vehicles_monthly <- df_electric_vehicles_agg %>% 
                               mutate(Date = as.Date(format(Date, "%Y-%m-01"))) %>%
                               group_by(Date) %>% 
                               summarize(Count = sum(Count), .groups = 'drop')

# Merge datasets
df_merged <- full_join(df_electric_vehicles_monthly, df_oil_monthly, by = "Date")

# Calculate the ratio for the secondary axis
max_values <- df_merged |> summarize(max_count = max(Count, na.rm = TRUE), max_price = max(Price, na.rm = TRUE))
ratio <- max_values$max_count / max_values$max_price

# Add the ratio-adjusted Price to the merged dataset
df_merged <- df_merged |> mutate(AdjustedPrice = Price * ratio)

# Plotting with smoothing and color changes
p <- ggplot(df_merged, aes(x = Date)) +
  geom_smooth(aes(y = Count, color = "Electric Vehicles Smoothed"), method = "loess", span = 0.2) +
  geom_line(aes(y = AdjustedPrice, color = "Oil Price")) +
  scale_y_continuous(
    "Number of Electric Vehicles",
    sec.axis = sec_axis(~ . / ratio, name = "Oil Price")
  ) +
  labs(title = "Comparison of Electric Vehicle Rise and Oil Prices Over Time",
       x = "Date", color = "Legend") +
  scale_color_manual(values = c("Electric Vehicles Smoothed" = "blue", "Oil Price" = "darkred")) +
  theme_minimal() +
  theme(legend.position = "bottom")

# Convert to interactive plot
interactive_plot <- ggplotly(p, width = 600, height = 400) %>%
  layout(legend = list(orientation = "h", x = 0.5, xanchor = "center", y = -0.3))

interactive_plot

4 Analysis

TO DO’s

Answers to the research questions
Different methods considered
Competing approaches
Justifications

4.1 RQ1

4.2 RQ2

Based on past electric vehicle adoption trends in Switzerland, can we forecast future adoption rates and pinpoint times of significant increases or decreases correlated with major events or policy changes?

Simple Linear Regression

With Oil

Choosing Dependent Variable, sales or EV registrations

Sales Data (from df_sales_EV):

Pros: Directly reflects market demand and consumer purchasing behavior. Cons: Can be influenced by short-term factors such as promotions or subsidies, which might not indicate long-term adoption trends.

Registration Data (from df_v_electric):

Pros: Represents actual additions to the vehicle population and can be more indicative of long-term trends in EV adoption. Cons: Might lag behind sales data, as registration occurs post-purchase and can be influenced by administrative processes.

interested in long-term trends in EV adoption and usage, registration data could be more appropriate.

The decision to remove rows with missing values was justified as the number of missing values in the Price column was relatively small (28 out of a larger dataset) and couldn’t be reliably imputed, ensuring that the analysis was performed on a more complete and consistent dataset.

Code

# Select the columns you want to keep in df_v_electric
df_v_electric <- df_v_electric %>%
  select(Location, Count, Date)

# Merge df_oil_monthly with df_v_electric based on the common 'Date' column
df_merged <- merge(df_v_electric, df_oil_monthly, by = "Date", all.x = TRUE)

# Remove rows with missing values in the 'Price' column
df_merged <- df_merged[complete.cases(df_merged), ]

Code

# Splitting the data into training and testing sets
set.seed(123) # for reproducibility
split_index <- sample(1:nrow(df_merged), 0.8 * nrow(df_merged))
train_data <- df_merged[split_index, ]
test_data <- df_merged[-split_index, ]

# Fitting the linear model
mod1_lin <- lm(Count ~ Price, data = train_data)

# Summary of the model to view coefficients and statistics
summary(mod1_lin)
#> 
#> Call:
#> lm(formula = Count ~ Price, data = train_data)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#>    -40    -40    -39    -27   5514 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 40.42528   10.10153    4.00  6.4e-05 ***
#> Price       -0.00542    0.12620   -0.04     0.97    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 222 on 4814 degrees of freedom
#> Multiple R-squared:  3.83e-07,   Adjusted R-squared:  -0.000207 
#> F-statistic: 0.00184 on 1 and 4814 DF,  p-value: 0.966

# Predicting with the test data
predictions <- predict(mod1_lin, test_data)
# Calculating residuals (difference between actual and predicted values)
residuals <- test_data$Count - predictions

# Calculating RMSE
rmse <- sqrt(mean(residuals^2))
print(paste("RMSE:", rmse))
#> [1] "RMSE: 247.396521283555"
# Creating a summary table for the linear model
table_regression <- tbl_regression(mod1_lin)

# Viewing the table
print(table_regression)
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#>       <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> CI = Confidence Interval</td>
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The linear regression analysis results indicate that the Price variable has a very weak or negligible effect on explaining the variation in the Count variable. Specifically:

The coefficient for Price is very close to zero (-0.00542), indicating that there is almost no linear relationship between Price and Count.

The p-value for Price (0.97) is much greater than the common significance level of 0.05. This high p-value suggests that Price is not statistically significant in predicting Count, as it fails to reject the null hypothesis that the coefficient is zero.

The R-squared value is very low (0.000523), indicating that the linear regression model explains only a negligible fraction of the variance in Count.

The Root Mean Squared Error (RMSE) is relatively high (247), suggesting that the model’s predictions have a substantial amount of error.

In summary, based on these results, it appears that the Price variable does not have a meaningful impact on predicting the Count variable, and the linear regression model is not suitable for explaining the relationship between these two variables. Further exploration of the data and potentially considering other factors or modeling approaches may be necessary to improve predictive accuracy.

with google trends

Code

# Merge the two datasets based on the common Date column
df_merged <- merge(df_merged, df_gtrends, by = "Date")
# Check for missing values in df_merged
#sum(is.na(df_merged))
# Check for missing values in each column of df_merged
#colSums(is.na(df_merged))

# Splitting the data into training and testing sets
set.seed(123) # for reproducibility
split_index <- sample(1:nrow(df_merged), 0.8 * nrow(df_merged))
train_data <- df_merged[split_index, ]
test_data <- df_merged[-split_index, ]

# Fitting the linear model
mod2_lin <- lm(Count ~ SearchRatio, data = train_data)

# Summary of the model to view coefficients and statistics
summary(mod2_lin)
#> 
#> Call:
#> lm(formula = Count ~ SearchRatio, data = train_data)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#>   -252    -39    -13      6   5425 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  -82.043      7.139   -11.5   <2e-16 ***
#> SearchRatio    5.957      0.314    19.0   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 214 on 4814 degrees of freedom
#> Multiple R-squared:  0.0695, Adjusted R-squared:  0.0693 
#> F-statistic:  359 on 1 and 4814 DF,  p-value: <2e-16

# Predicting with the test data
predictions <- predict(mod2_lin, test_data)

# Calculating residuals (difference between actual and predicted values)
residuals <- test_data$Count - predictions

# Calculating RMSE
rmse <- sqrt(mean(residuals^2))
print(paste("RMSE:", rmse))
#> [1] "RMSE: 240.599640776535"

Overall, while there is a statistically significant relationship between SearchRatio and Count, the model’s R-squared value suggests that other factors not included in the model may also influence the Count variable. You may want to explore additional variables or more complex models to improve predictive accuracy.

Multivariable Regression

with Demographic groups, Oil Price and Google Trend

Code

# Merge the data frames on the 'Year' column
df_merged <- merge(df_merged, df_demographic, by.x = "Date", by.y = "Year")

# Splitting the data into training and testing sets
set.seed(123) # for reproducibility
split_index <- sample(1:nrow(df_merged), 0.8 * nrow(df_merged))
train_data <- df_merged[split_index, ]
test_data <- df_merged[-split_index, ]

# Fitting the multivariable linear model
mod1_multi <- lm(Count ~ SearchRatio + Price + `Generation Z` + Millennials + `Generation X` + `Baby Boomers`, data = df_merged)

# Summary of the model to view coefficients and statistics
summary(mod1_multi)
#> 
#> Call:
#> lm(formula = Count ~ SearchRatio + Price + `Generation Z` + Millennials + 
#>     `Generation X` + `Baby Boomers`, data = df_merged)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#> -132.8  -13.9   -4.8    3.7 2068.2 
#> 
#> Coefficients:
#>                 Estimate Std. Error t value Pr(>|t|)  
#> (Intercept)     1.32e+02   1.06e+03    0.12    0.901  
#> SearchRatio     1.04e+00   1.33e+00    0.78    0.437  
#> Price           4.10e-02   2.83e-01    0.14    0.885  
#> `Generation Z`  1.38e-03   1.80e-03    0.77    0.442  
#> Millennials    -3.64e-04   4.96e-04   -0.73    0.464  
#> `Generation X` -1.02e-03   7.95e-04   -1.28    0.201  
#> `Baby Boomers`  6.32e-04   2.66e-04    2.38    0.018 *
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 117 on 497 degrees of freedom
#> Multiple R-squared:  0.0924, Adjusted R-squared:  0.0815 
#> F-statistic: 8.43 on 6 and 497 DF,  p-value: 9.9e-09

# Predicting with the test data
predictions <- predict(mod1_multi, test_data)

# Evaluating the model
# Calculating R-squared value
r_squared <- cor(test_data$Count, predictions)^2
cat("R-squared: ", r_squared, "\n")
#> R-squared:  0.365

# Calculating RMSE (Root Mean Squared Error)
rmse <- sqrt(mean((predictions - test_data$Count)^2))
cat("RMSE: ", rmse, "\n")
#> RMSE:  35.6

# Create a summary table for the linear model
table_regression_mod1_multi <- tbl_regression(mod1_multi)

# Print the table
print(table_regression_mod1_multi)
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#>   <thead>
#>     
#>     <tr class="gt_col_headings">
#>       <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="&lt;strong&gt;Characteristic&lt;/strong&gt;"><strong>Characteristic</strong></th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="&lt;strong&gt;Beta&lt;/strong&gt;"><strong>Beta</strong></th>
#>       <th class="gt_col_heading gt_columns_bottom_border gt_center" rowspan="1" colspan="1" scope="col" id="&lt;strong&gt;95% CI&lt;/strong&gt;&lt;span class=&quot;gt_footnote_marks&quot; style=&quot;white-space:nowrap;font-style:italic;font-weight:normal;&quot;&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;"><strong>95% CI</strong><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span></th>
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#>   <tbody class="gt_table_body">
#>     <tr><td headers="label" class="gt_row gt_left">SearchRatio</td>
#> <td headers="estimate" class="gt_row gt_center">1.0</td>
#> <td headers="ci" class="gt_row gt_center">-1.6, 3.7</td>
#> <td headers="p.value" class="gt_row gt_center">0.4</td></tr>
#>     <tr><td headers="label" class="gt_row gt_left">Price</td>
#> <td headers="estimate" class="gt_row gt_center">0.04</td>
#> <td headers="ci" class="gt_row gt_center">-0.52, 0.60</td>
#> <td headers="p.value" class="gt_row gt_center">0.9</td></tr>
#>     <tr><td headers="label" class="gt_row gt_left">Generation Z</td>
#> <td headers="estimate" class="gt_row gt_center">0.00</td>
#> <td headers="ci" class="gt_row gt_center">0.00, 0.00</td>
#> <td headers="p.value" class="gt_row gt_center">0.4</td></tr>
#>     <tr><td headers="label" class="gt_row gt_left">Millennials</td>
#> <td headers="estimate" class="gt_row gt_center">0.00</td>
#> <td headers="ci" class="gt_row gt_center">0.00, 0.00</td>
#> <td headers="p.value" class="gt_row gt_center">0.5</td></tr>
#>     <tr><td headers="label" class="gt_row gt_left">Generation X</td>
#> <td headers="estimate" class="gt_row gt_center">0.00</td>
#> <td headers="ci" class="gt_row gt_center">0.00, 0.00</td>
#> <td headers="p.value" class="gt_row gt_center">0.2</td></tr>
#>     <tr><td headers="label" class="gt_row gt_left">Baby Boomers</td>
#> <td headers="estimate" class="gt_row gt_center">0.00</td>
#> <td headers="ci" class="gt_row gt_center">0.00, 0.00</td>
#> <td headers="p.value" class="gt_row gt_center">0.018</td></tr>
#>   </tbody>
#>   
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#>     <tr>
#>       <td class="gt_footnote" colspan="4"><span class="gt_footnote_marks" style="white-space:nowrap;font-style:italic;font-weight:normal;"><sup>1</sup></span> CI = Confidence Interval</td>
#>     </tr>
#>   </tfoot>
#> </table>
#> </div>

This multivariate linear regression model does not appear to have a strong explanatory power, as indicated by the low adjusted R-squared value and the relatively high RMSE. Additionally, only the Baby Boomers variable shows statistical significance in predicting the Count, while other variables do not appear to be significant in this context. You may want to consider further refining the model or exploring other variables to improve its predictive performance.

Adding Political Parties

Code

# Remove 'Confederation' from df_merged
df_multi_reg <- df_merged[df_merged$Location != 'Confederation', ]

# Step 2: Aggregate data for 'Switzerland' in politic data, For each year, sum the values of the 26 cantons to create a combined observation labeled "Switzerland". and then divide by 26
political_combined_data$Year <- year(ymd(political_combined_data$Year))
df_politics <- political_combined_data %>%
  group_by(Year) %>%
  summarise(across(c(`Against`, `Slightly Against`, `Neutral`, `Slightly in Favour`, `In Favour`), sum, na.rm = TRUE)) %>%
  mutate(Canton = 'Switzerland')
#> Warning: There was 1 warning in `summarise()`.
#> i In argument: `across(...)`.
#> i In group 1: `Year = 1999`.
#> Caused by warning:
#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
#> Supply arguments directly to `.fns` through an anonymous function
#> instead.
#> 
#>   # Previously
#>   across(a:b, mean, na.rm = TRUE)
#> 
#>   # Now
#>   across(a:b, \(x) mean(x, na.rm = TRUE))

# Append the new 'Switzerland' rows to df_politics
df_politics <- bind_rows(political_combined_data, df_politics)

# List of column names you want to divide by 26
columns_to_divide <- c("Against", "Slightly Against", "Neutral", "Slightly in Favour", "In Favour")

df_politics <- df_politics %>%
  mutate_at(columns_to_divide, list(~./26))

# Step 3: Align 'Date' in df_multi_reg to 'Year' in df_politics 
df_multi_reg$Year <- year(ymd(df_multi_reg$Date))
df_multi_reg <- select(df_multi_reg, -Date)

#Merge the datasets on the aligned 'Location/Canton' and 'Date/Year' columns.
df_politics$Location <- df_politics$Canton 

# Step 4: Merge the datasets on 'Canton' and 'Year'
df_multi_reg <- merge(df_multi_reg, df_politics, by = c("Location", "Year"), all = FALSE)

# Reorder columns in a data frame
df_multi_reg <- df_multi_reg %>%
  select(Count, everything())

df_multi_reg <- df_multi_reg %>% select(-Canton, -KANTONSNUM)
any(is.na(df_multi_reg))
#> [1] FALSE

# Perform regression analysis without 'Location'
full_model <- lm(Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + `Generation X` + `Baby Boomers` + Against + `Slightly Against` + Neutral + `Slightly in Favour` + `In Favour`, data = df_multi_reg)

# Perform backward elimination
reduced_model <- step(full_model, direction = "backward")
#> Start:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + 
#>     `Generation X` + `Baby Boomers` + Against + `Slightly Against` + 
#>     Neutral + `Slightly in Favour` + `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + 
#>     `Generation X` + Against + `Slightly Against` + Neutral + 
#>     `Slightly in Favour` + `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + 
#>     Against + `Slightly Against` + Neutral + `Slightly in Favour` + 
#>     `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Against + 
#>     `Slightly Against` + Neutral + `Slightly in Favour` + `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + Against + `Slightly Against` + 
#>     Neutral + `Slightly in Favour` + `In Favour`
#> 
#>                        Df Sum of Sq   RSS AIC
#> - Against               1       101 68999 714
#> - `Slightly in Favour`  1       112 69010 714
#> - Year                  1       537 69435 714
#> <none>                              68898 715
#> - SearchRatio           1      1555 70453 716
#> - Price                 1      1668 70566 716
#> - Neutral               1      6034 74932 723
#> - `Slightly Against`    1     17063 85961 737
#> - `In Favour`           1     22929 91828 745
#> 
#> Step:  AIC=714
#> Count ~ Year + Price + SearchRatio + `Slightly Against` + Neutral + 
#>     `Slightly in Favour` + `In Favour`
#> 
#>                        Df Sum of Sq    RSS AIC
#> - `Slightly in Favour`  1        78  69077 712
#> - Year                  1       463  69462 712
#> <none>                               68999 714
#> - SearchRatio           1      1459  70459 714
#> - Price                 1      1624  70624 714
#> - Neutral               1      6289  75289 721
#> - `Slightly Against`    1     17073  86073 736
#> - `In Favour`           1     66523 135523 785
#> 
#> Step:  AIC=712
#> Count ~ Year + Price + SearchRatio + `Slightly Against` + Neutral + 
#>     `In Favour`
#> 
#>                      Df Sum of Sq    RSS AIC
#> - Year                1       478  69556 711
#> <none>                             69077 712
#> - SearchRatio         1      1484  70561 712
#> - Price               1      1696  70773 712
#> - Neutral             1      6457  75534 719
#> - `Slightly Against`  1     18225  87303 735
#> - `In Favour`         1     66469 135547 783
#> 
#> Step:  AIC=711
#> Count ~ Price + SearchRatio + `Slightly Against` + Neutral + 
#>     `In Favour`
#> 
#>                      Df Sum of Sq    RSS AIC
#> - Price               1      1257  70813 710
#> <none>                             69556 711
#> - SearchRatio         1      3651  73207 714
#> - Neutral             1      6957  76513 719
#> - `Slightly Against`  1     17771  87326 733
#> - `In Favour`         1     65991 135547 781
#> 
#> Step:  AIC=710
#> Count ~ SearchRatio + `Slightly Against` + Neutral + `In Favour`
#> 
#>                      Df Sum of Sq    RSS AIC
#> <none>                             70813 710
#> - SearchRatio         1      4023  74836 714
#> - Neutral             1      6722  77535 718
#> - `Slightly Against`  1     17769  88582 733
#> - `In Favour`         1     65625 136438 779

# View the summary of the reduced model
summary(reduced_model)
#> 
#> Call:
#> lm(formula = Count ~ SearchRatio + `Slightly Against` + Neutral + 
#>     `In Favour`, data = df_multi_reg)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -111.61  -10.32   -2.99    8.03  153.23 
#> 
#> Coefficients:
#>                    Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)         -12.858      6.017   -2.14   0.0350 *  
#> SearchRatio           0.827      0.342    2.42   0.0173 *  
#> `Slightly Against`  -10.300      2.026   -5.08  1.7e-06 ***
#> Neutral             -18.494      5.915   -3.13   0.0023 ** 
#> `In Favour`          20.121      2.059    9.77  2.4e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 26.2 on 103 degrees of freedom
#> Multiple R-squared:  0.699,  Adjusted R-squared:  0.687 
#> F-statistic: 59.7 on 4 and 103 DF,  p-value: <2e-16

# Perform backward elimination
reduced_model <- step(full_model, direction = "backward")
#> Start:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + 
#>     `Generation X` + `Baby Boomers` + Against + `Slightly Against` + 
#>     Neutral + `Slightly in Favour` + `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + 
#>     `Generation X` + Against + `Slightly Against` + Neutral + 
#>     `Slightly in Favour` + `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Millennials + 
#>     Against + `Slightly Against` + Neutral + `Slightly in Favour` + 
#>     `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + `Generation Z` + Against + 
#>     `Slightly Against` + Neutral + `Slightly in Favour` + `In Favour`
#> 
#> 
#> Step:  AIC=715
#> Count ~ Year + Price + SearchRatio + Against + `Slightly Against` + 
#>     Neutral + `Slightly in Favour` + `In Favour`
#> 
#>                        Df Sum of Sq   RSS AIC
#> - Against               1       101 68999 714
#> - `Slightly in Favour`  1       112 69010 714
#> - Year                  1       537 69435 714
#> <none>                              68898 715
#> - SearchRatio           1      1555 70453 716
#> - Price                 1      1668 70566 716
#> - Neutral               1      6034 74932 723
#> - `Slightly Against`    1     17063 85961 737
#> - `In Favour`           1     22929 91828 745
#> 
#> Step:  AIC=714
#> Count ~ Year + Price + SearchRatio + `Slightly Against` + Neutral + 
#>     `Slightly in Favour` + `In Favour`
#> 
#>                        Df Sum of Sq    RSS AIC
#> - `Slightly in Favour`  1        78  69077 712
#> - Year                  1       463  69462 712
#> <none>                               68999 714
#> - SearchRatio           1      1459  70459 714
#> - Price                 1      1624  70624 714
#> - Neutral               1      6289  75289 721
#> - `Slightly Against`    1     17073  86073 736
#> - `In Favour`           1     66523 135523 785
#> 
#> Step:  AIC=712
#> Count ~ Year + Price + SearchRatio + `Slightly Against` + Neutral + 
#>     `In Favour`
#> 
#>                      Df Sum of Sq    RSS AIC
#> - Year                1       478  69556 711
#> <none>                             69077 712
#> - SearchRatio         1      1484  70561 712
#> - Price               1      1696  70773 712
#> - Neutral             1      6457  75534 719
#> - `Slightly Against`  1     18225  87303 735
#> - `In Favour`         1     66469 135547 783
#> 
#> Step:  AIC=711
#> Count ~ Price + SearchRatio + `Slightly Against` + Neutral + 
#>     `In Favour`
#> 
#>                      Df Sum of Sq    RSS AIC
#> - Price               1      1257  70813 710
#> <none>                             69556 711
#> - SearchRatio         1      3651  73207 714
#> - Neutral             1      6957  76513 719
#> - `Slightly Against`  1     17771  87326 733
#> - `In Favour`         1     65991 135547 781
#> 
#> Step:  AIC=710
#> Count ~ SearchRatio + `Slightly Against` + Neutral + `In Favour`
#> 
#>                      Df Sum of Sq    RSS AIC
#> <none>                             70813 710
#> - SearchRatio         1      4023  74836 714
#> - Neutral             1      6722  77535 718
#> - `Slightly Against`  1     17769  88582 733
#> - `In Favour`         1     65625 136438 779

# View the summary of the reduced model
summary(reduced_model)
#> 
#> Call:
#> lm(formula = Count ~ SearchRatio + `Slightly Against` + Neutral + 
#>     `In Favour`, data = df_multi_reg)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -111.61  -10.32   -2.99    8.03  153.23 
#> 
#> Coefficients:
#>                    Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)         -12.858      6.017   -2.14   0.0350 *  
#> SearchRatio           0.827      0.342    2.42   0.0173 *  
#> `Slightly Against`  -10.300      2.026   -5.08  1.7e-06 ***
#> Neutral             -18.494      5.915   -3.13   0.0023 ** 
#> `In Favour`          20.121      2.059    9.77  2.4e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 26.2 on 103 degrees of freedom
#> Multiple R-squared:  0.699,  Adjusted R-squared:  0.687 
#> F-statistic: 59.7 on 4 and 103 DF,  p-value: <2e-16

Starting Model: The initial model includes a wide range of predictor variables: Year, Price, SearchRatio, various generational groups (Generation Z, Millennials, Generation X, Baby Boomers), and several categories of opinions (Against, Slightly Against, Neutral, Slightly in Favour, In Favour).
Stepwise Process: The stepwise procedure iteratively removes the least significant variables based on their contribution to the model (using the Akaike Information Criterion, AIC, as a guide). A lower AIC indicates a better model fit with respect to the number of variables.
Model Refinement: As we progress through the steps, we observe the removal of several variables like Baby Boomers, Millennials, and Generation X. This suggests that these variables were not significantly contributing to the explanation of variations in Count.
Final Model: The last step shows the model with the variables: SearchRatio, Slightly Against, Neutral, and In Favour. This model has an AIC of 710, which is lower than the starting AIC of 715, indicating a more efficient model.
Significance of Remaining Variables: In the final model, all the variables are considered significant contributors. If any were to be removed, it would result in a higher AIC, indicating a less optimal model.
Interpretation of Variables:
- SearchRatio, Slightly Against, Neutral, and In Favour are significant predictors for Count.
- The absence of demographic variables in the final model suggests that the adoption count may not be strongly related to these demographic factors, or their effect is captured by other variables.
- The presence of opinion-related variables (like In Favour) indicates a possible correlation between public opinion and the count of the dependent variable (possibly related to EV adoption or similar context).
Cautions: While stepwise regression is useful for variable selection, it can sometimes lead to overfitting or neglecting important variables that don’t show strong individual effects but are important in combination with others. Hence, the results should be interpreted with caution, and further analysis (like checking for multicollinearity, interactions between variables, etc.) is recommended to validate the findings.

4.3 RQ3

In comparing regions in Switzerland, which areas show higher or lower adoption of electric vehicles, and how does this regional adoption align or vary with external factors like oil price changes, political opinions, and demographic shifts?

From the initial data:

In 2005, the EV adoption rate per capita was approximately 0.000004, which means there were about 4 EVs per million people. By 2009, this rate increased to approximately 18 EVs per million people.

This approach will give us a general sense of EV adoption in relation to the overall population but won’t provide regional demographic granularity.

The merged data now includes the total population for each year in Switzerland and the total count of electric vehicles (EVs) for those years. We have also calculated the EV adoption rate per capita, which gives us an insight into how EV adoption scales with the population size.

These figures show a growing trend in EV adoption in relation to the population size, albeit the numbers are still quite small relative to the total population.

The trend shows a gradual increase in EV adoption relative to the population size, indicating a growing acceptance and usage of electric vehicles in Switzerland during this period.

Code

df_demo <- df_demographic
df_ev <- df_v_electric
# Convert Date and Year to Date type
df_ev$Date <- as.Date(df_ev$Date)
df_demo$Year <- as.Date(df_demo$Year)

# Summing up the population for each year
df_demo$total_population <- rowSums(df_demo[,c("Generation Z", "Millennials", "Generation X", "Baby Boomers")])

# Aggregating EV data by year
df_ev_yearly <- df_ev %>%
  group_by(Year = as.Date(format(Date, "%Y-01-01"))) %>%
  summarize(total_ev = sum(Count))

# Merging the datasets
merged_data <- merge(df_ev_yearly, df_demo, by = "Year")

# Calculating EV adoption per capita
merged_data$ev_per_capita <- merged_data$total_ev / merged_data$total_population

# Creating an interactive plot
p <- ggplot(merged_data, aes(x = Year, y = ev_per_capita)) +
  geom_line() +
  labs(title = "EV Adoption Per Capita Over Time in Switzerland",
       x = "Year",
       y = "EV Adoption Per Capita")

# Convert to interactive plot and adjust legend
interactive_plot <- ggplotly(p, width = 600, height = 400) %>%
  layout(legend = list(orientation = 'h', x = 0.5, xanchor = 'center', y = -0.15))

# To display the plot in an R environment, you can simply call the variable
interactive_plot

The correlation matrix below shows the relationships between the proportions of different generational groups (Generation Z, Millennials, Generation X, Baby Boomers) and the EV adoption rate per capita in Switzerland. The heatmap provides the following insights:

The correlation coefficients indicate the strength and direction of the relationship between each pair of variables. Positive values suggest a positive correlation (as one increases, so does the other), while negative values suggest an inverse relationship.

*babyboomers !

Code

# Data Preparation
df_ev$Date <- as.Date(df_ev$Date)
df_demo$Year <- as.Date(df_demo$Year)

# Summing up the population for each year
df_demo$total_population <- rowSums(df_demo[,c("Generation Z", "Millennials", "Generation X", "Baby Boomers")])

# Calculate proportions
df_demo$prop_gen_z <- df_demo$`Generation Z` / df_demo$total_population
df_demo$prop_millennials <- df_demo$Millennials / df_demo$total_population
df_demo$prop_gen_x <- df_demo$`Generation X` / df_demo$total_population
df_demo$prop_boomers <- df_demo$`Baby Boomers` / df_demo$total_population

# Aggregating EV data by year
df_ev_yearly <- df_ev %>%
  group_by(Year = as.Date(format(Date, "%Y-01-01"))) %>%
  summarize(total_ev = sum(Count))

# Merging the datasets
merged_data <- merge(df_ev_yearly, df_demo, by = "Year")

# Calculating EV adoption per capita
merged_data$ev_per_capita <- merged_data$total_ev / merged_data$total_population

# Correlation Matrix
correlation_matrix <- cor(merged_data[,c("prop_gen_z", "prop_millennials", "prop_gen_x", "prop_boomers", "ev_per_capita")])

# Melting the correlation matrix for ggplot
melted_correlation_matrix <- melt(correlation_matrix)
#> Warning in melt(correlation_matrix): The melt generic in data.table
#> has been passed a matrix and will attempt to redirect to the
#> relevant reshape2 method; please note that reshape2 is deprecated,
#> and this redirection is now deprecated as well. To continue using
#> melt methods from reshape2 while both libraries are attached, e.g.
#> melt.list, you can prepend the namespace like
#> reshape2::melt(correlation_matrix). In the next version, this
#> warning will become an error.

# Creating the heatmap
# Creating the interactive heatmap
p <- ggplot(melted_correlation_matrix, aes(Var1, Var2, fill = value)) +
  geom_tile() +
  geom_text(aes(label = sprintf("%.2f", value)), color = "white", size = 4) +
  scale_fill_gradient(low = "lightblue", high = "darkblue", name = "Correlation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  labs(x = "", y = "", title = "Interactive Correlation Heatmap")

# Convert to interactive plot
ggplotly(p, tooltip = c("label", "fill"))

4.4 RQ4

How has the growth of electric vehicles evolved in comparison to other countries such as France, and what factors might account for the differences in their evolution ?

We have

Code

# Filtering Swiss data for specific fuel types
swiss_specific_fuel <- df_v %>%
  filter(Fuel %in% c("Diesel", "Electricity", "Conventional hybrid", "Plug-in hybrid", "Petrol")) %>%
  filter(Location == 'Switzerland') |>
  filter(VehicleType == 'Passenger car') |>
  filter(Date > as.Date('2012-01-01')) |>
  filter(Date < as.Date('2021-12-31'))

# Selecting equivalent columns from the French dataset
french_specific_fuel <- df_v_fr %>%
  select(Date, Diesel_delta, Essence_delta, Conventional_Hybrid_delta, Plug_in_Hybrid_delta, Electrique_delta) # Adjust column names accordingly

# Reshape French dataset to long format for easier plotting
french_specific_fuel_long <- french_specific_fuel %>%
  pivot_longer(cols = -Date, names_to = "Fuel", values_to = "Count")

# Standardize counts in each dataset
swiss_specific_fuel <- swiss_specific_fuel %>%
  mutate(Count = scale(Count))

french_specific_fuel_long <- french_specific_fuel_long %>%
  mutate(Count = scale(Count))

# Rename the 'Fuel' column in the French dataset
french_specific_fuel_long <- french_specific_fuel_long %>%
  mutate(Fuel = case_when(
    Fuel == "Diesel_delta" ~ "Diesel",
    Fuel == "Essence_delta" ~ "Petrol",
    Fuel == "Conventional_Hybrid_delta" ~ "Conventional hybrid",
    Fuel == "Plug_in_Hybrid_delta" ~ "Plug-in hybrid",
    Fuel == "Electrique_delta" ~ "Electricity"
  ))

# Define color palette for each fuel type
fuel_colors <- c("Diesel" = "black", "Electricity" = "green", "Conventional hybrid" = "purple", "Plug-in hybrid" = "blue", "Petrol" = "orange")

p <- ggplot() +
  geom_smooth(data = swiss_specific_fuel, aes(x = Date, y = Count, color = Fuel), 
              method = "loess", se = FALSE, size = 1.5) +
  geom_line(data = french_specific_fuel_long, aes(x = Date, y = Count, color = Fuel), 
            alpha = 0.4, size = 0.8) +
  scale_color_manual(values = fuel_colors, 
                     labels = c("Diesel", "Electricity", "Conventional hybrid", 
                                "Plug-in hybrid", "Petrol"),
                     breaks = c("Diesel", "Electricity", "Conventional hybrid", 
                                "Plug-in hybrid", "Petrol")) +
  labs(x = "Date", y = "Standardized Count", color = "Fuel Type") +
  theme_minimal() +
  geom_text(data = data.frame(x = as.Date("2021-01-01"), y = c(3, 2.8), 
                              label = c("Switzerland has", "the thickest line")), 
            aes(x = x, y = y, label = label, color = label), 
            size = 4, show.legend = FALSE)

interactive_plot <- ggplotly(p, width = 600, height = 400, tooltip = c("x", "y", "color"))
interactive_plot <- interactive_plot %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))
interactive_plot

Code

# Filtering Swiss data for specific fuel types
swiss_specific_fuel <- df_v %>%
  filter(Fuel %in% c("Electricity", "Conventional hybrid", "Plug-in hybrid")) %>%
  filter(Location == 'Switzerland' & VehicleType == 'Passenger car' & Date > as.Date('2012-01-01') & Date < as.Date('2021-12-31'))

# Selecting equivalent columns from the French dataset
french_specific_fuel <- df_v_fr %>%
  select(Date, Diesel_delta, Essence_delta, Conventional_Hybrid_delta, Plug_in_Hybrid_delta, Electrique_delta) # Adjust column names accordingly

# Reshape French dataset to long format for easier plotting
french_specific_fuel_long <- french_specific_fuel %>%
  pivot_longer(cols = -Date, names_to = "Fuel", values_to = "Count") %>%
  mutate(Fuel = case_when(
    Fuel == "Conventional_Hybrid_delta" ~ "Conventional hybrid",
    Fuel == "Plug_in_Hybrid_delta" ~ "Plug-in hybrid",
    Fuel == "Electrique_delta" ~ "Electricity"
  ))

# Standardize counts in each dataset
swiss_specific_fuel <- swiss_specific_fuel %>%
  mutate(Count = scale(Count))

french_specific_fuel_long <- french_specific_fuel_long %>%
  filter(Fuel %in% c("Conventional hybrid", "Plug-in hybrid", "Electricity")) %>%
  mutate(Count = scale(Count))

# Define color palette for each fuel type
fuel_colors <- c("Conventional hybrid" = "purple", "Plug-in hybrid" = "blue", "Electricity" = "green")

# Plotting with faceting and improved axis text
p <- ggplot() +
  geom_smooth(data = swiss_specific_fuel, aes(x = Date, y = Count, color = Fuel), 
              method = "loess", se = FALSE, size = 1.5) +
  geom_line(data = french_specific_fuel_long, aes(x = Date, y = Count, color = Fuel), 
            alpha = 0.4, size = 0.8) +
  scale_color_manual(values = fuel_colors, labels = c("Switzerland", "France")) +
  labs(x = "Date", y = "Standardized Count") +
  theme_minimal() +
  theme(legend.position = "bottom",
        strip.background = element_blank(),
        strip.text.x = element_text(size = 10, angle = 0),
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        axis.title.x = element_text(size = 12, margin = margin(t = 10)),
        axis.title.y = element_text(size = 12, margin = margin(r = 10))) +
  facet_wrap(~Fuel, scales = 'free_y', ncol = 1)

# Convert to interactive plot
interactive_plot <- ggplotly(p, width = 600, height = 600, tooltip = c("x", "y", "color"))
interactive_plot <- interactive_plot %>%
  layout(legend = list(orientation = "h", x = 0, xanchor = "left", y = -0.2))
interactive_plot

Switzerland has the thickest line

This compares the rate at which a certain category of fuel is adopted by the citizen of each country (Switzerland vs. France) and is normalized for each country’s size.

We can see that Electricity and Plug-in Hybrid follow roughly the same trajectory. This could be caused by the fact that they are used in a very similar way. In both these categories Switzerland has a slightly higher adoption rate.

For Conventional hybrid however, France seems to have a slightly faster adopting rate.

4.5 RQ5

To what extent does the evolution in the availability of charging stations exert an influence on the adoption of electric vehicles in Switzerland? ::: {.cell layout-align=“center”}

Code

# First, let's merge the df_v and df_charge_number_CH data sets, and we will look at Fuel: Electricity

df_v_electric_total_ch <- df_v %>%
  filter(Fuel == "Electricity", VehicleType == "Passenger car", Location == c("Switzerland","Confederation")) %>%
  select(Date, Count)

sum_by_year <- df_v_electric_total_ch %>%
  group_by(Year = lubridate::year(Date)) %>%
  summarise(Total_Count = sum(Count))


# Convert year to a common format for merging
sum_by_year <- sum_by_year %>%
  mutate(year = as.Date(paste0(Year, "-01-01")))

# Merge the datasets based on the "year" column
merged_v_charge <- left_join(sum_by_year, df_charge_number_CH, by = c("year" = "year"))

# cleaning merged data set
merged_v_charge <- merged_v_charge %>%
  filter(Year > "2011") %>%
  select(Year, Total_Count, powertrain, value)

names(merged_v_charge)[names(merged_v_charge) == "Total_count"] <- "EVs"
colnames(merged_v_charge)[colnames(merged_v_charge) == "value"] <- "Charging station"

# Summing Powertrain together
merged_v_charge <- merged_v_charge %>%
  group_by(Year, Total_Count) %>%
  summarise(Count = sum(`Charging station`))

# Checking the correlation
corr_charge_ev <- cor(merged_v_charge$Total_Count, merged_v_charge$Count)

# their correlation is 0.957, almost perfectly correlated (no suprise here)

# Checking for lagged correlation

lags_to_explore <- 1:3

lagged_correlation <- function(data, lag) {
  data %>%
    mutate(Count_Lagged = lag(Count, n = lag, default = NA)) %>%
    summarise(Correlation = cor(Total_Count, Count_Lagged, use = "complete.obs"))
}

# Calculate lagged correlations for each lag
lagged_correlations_df <- data.frame(Lag = lags_to_explore) %>%
  rowwise() %>%
  mutate(Correlation = lagged_correlation(merged_v_charge[, -1], Lag)$Correlation)

# Print the results
print("Original Correlation:")
#> [1] "Original Correlation:"
print(corr_charge_ev)
#> [1] 0.957

print("Lagged Correlations:")
#> [1] "Lagged Correlations:"
print(lagged_correlations_df)
#> # A tibble: 3 x 2
#> # Rowwise: 
#>     Lag Correlation
#>   <int>       <dbl>
#> 1     1       0.943
#> 2     2       0.910
#> 3     3       0.885


# Now we formulate the following Hypothesis

# H0: new charging station increase EV adoption vs. H1: new charging station does not increase EV adoption

# Check these hypotheses with a simple linear regression
linear_charging <- lm(Total_Count ~ Count, data = merged_v_charge)

# Poisson Test
poisson_model <- glm(Total_Count ~ Count, family = poisson, data = merged_v_charge)

# Set up the layout using mfrow
par(mfrow = c(1, 2))  # 1 row, 2 columns

# Plotting for Simple Linear Regression
plot(linear_charging, 1, main = "LM Residuals vs Fitted")
plot(linear_charging, 2, main = "LM Normal Q-Q Plot")
plot(linear_charging, 3, main = "LM Scale-Location Plot")
plot(linear_charging, 5, main = "LM Residuals vs Leverage")

# Plotting for Poisson Regression
plot(poisson_model, which = 1, main = "Poisson Residuals vs Fitted")
plot(poisson_model, which = 2, main = "Poisson Normal Q-Q Plot")
plot(poisson_model, which = 3, main = "Poisson Scale-Location Plot")
plot(poisson_model, which = 5, main = "Poisson Residuals vs Leverage")

# Printing
summary(linear_charging)
#> 
#> Call:
#> lm(formula = Total_Count ~ Count, data = merged_v_charge)
#> 
#> Residuals:
#>    Min     1Q Median     3Q    Max 
#>  -6059  -2966  -1298   3627   4916 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) -5340.399   2122.178   -2.52    0.033 *  
#> Count           3.341      0.338    9.88  3.9e-06 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 4150 on 9 degrees of freedom
#> Multiple R-squared:  0.916,  Adjusted R-squared:  0.906 
#> F-statistic: 97.7 on 1 and 9 DF,  p-value: 3.94e-06
summary(poisson_model)
#> 
#> Call:
#> glm(formula = Total_Count ~ Count, family = poisson, data = merged_v_charge)
#> 
#> Coefficients:
#>             Estimate Std. Error z value Pr(>|z|)    
#> (Intercept) 7.46e+00   7.67e-03     973   <2e-16 ***
#> Count       2.68e-04   7.91e-07     339   <2e-16 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for poisson family taken to be 1)
#> 
#>     Null deviance: 139780.3  on 10  degrees of freedom
#> Residual deviance:   6337.1  on  9  degrees of freedom
#> AIC: 6457
#> 
#> Number of Fisher Scoring iterations: 4
# Reset the layout
par(mfrow = c(1, 1))

::: The year-on-year correlation is the highest, the lagged correlation diminishes. We could suggest hat the availability of charging stations and the new registration of EVs go hand-in-hand, and that the availability of new charging station does not create a demand of new EVs by itself. Correlation does not imply causation, while we see a relationship, we can’t conclude that charging stations directly cause changes in electric vehicle adoption only with a Correlation analysis

With both a linear regression and a Poisson-test. We find evidence of statistically significant relationship between the count of available charging station and the count of electric vehicles registered. We have Prediction variable/coefficient of 2.68 x 10^-4 and 3.34 respectively. And a p-val < 0.005 for both. However, it is important to remind ourselves that these variables have a bidirectional / mutual influence, beyond the scope of what our analysis shows. The relationship is not strictly unidirectional and therefore, it is hard to conclude anything without further domain-knowledge and context-specific information

5 Conclusion

TO DO’s

Take home message
Limitations
Future work?

1 Introduction

1.1 Background and Motivation

1.2 Related Work

1.3 Reseach Questions

2 Data

2.1 Raw Datasets

2.1.1 Swiss Vehicle Registration Dataset

2.1.2 Oil price Dataset

2.1.3 Swiss Demographics Dataset

2.1.4 Google trend data

2.1.5 French vehicle registration data

2.1.6 Charging Stations

2.2.8 Political strength per canton

2.2.7 Swiss Population

2.2 Data Wrangling

2.2.1 Data Wrangling : Oil clean

2.2.2 Data Wrangling : Google trend clean

2.2.3 Cleaning of demographic_data

2.2.4 Data Wrangling : Swiss vehicle clean

Redundancy

Merging and Joining

Refinement and Vehicle Classification Insights

Rows with 0

2.2.5 Data wrangling French vehicle clean

2.2.6 Availability of charging stations

2.2.7 Availability of charging station in France and Switzerland

2.2.8 Political strength per canton

2.2.9 Swiss Population

3 Exploratory data analysis

3.1 Switzerland

3.1.1 seasonality

3.1.2 Vehicule Registration by Fuel time over time

3.1.3 Availability of Charging station

3.1.3.1 Availability of Charging station in Switzerland

3.1.3.2 Availability of Charging station in France

3.1.4 Map

3.1.4.1 Count of Electricity car Registration for all years per cantons

3.1.4.2 Count of Electricity car Registration for all years per cantons Standardized

3.2 Google Trend

3.3 Oil

3.4 Demographics

3.5 French vehicles

3.5.1 Total vehicles evolution France

3.5.2 Deltas evolution

3.6 Political Parties

3.7 Swiss vs France

3.7.1 Electric vs Hybrid vs Petrol

3.8 EV and Google Trends

3.9 EV and Oil Price

4 Analysis

4.1 RQ1

4.2 RQ2

Simple Linear Regression

With Oil

with google trends

Multivariable Regression

with Demographic groups, Oil Price and Google Trend

Adding Political Parties

4.3 RQ3

4.4 RQ4

4.5 RQ5

5 Conclusion